Lin Yang1, Hui Wu2, Brenda C M de Winter3, Chang-Cheng Sheng4,5, Hong-Qiang Qiu6, Yu Cheng6, Juan Chen1, Qiu-Ling Zhao1, Jing Huang1, Zheng Jiao7,8, Rui-Xiang Xie9. 1. Department of Pharmacy, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuma Road 420, Fuzhou, 350014, China. 2. Department of Medical Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, China. 3. Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. 4. Department of Pharmacy, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Shanghai, 200040, China. 5. Department of Pharmacy, Guizhou Provincial People's Hospital, Guiyang, China. 6. Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China. 7. Department of Pharmacy, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Shanghai, 200040, China. zjiao@fudan.edu.cn. 8. Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, No. 241 West Huaihai Road, Shanghai, 200030, China. zjiao@fudan.edu.cn. 9. Department of Pharmacy, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuma Road 420, Fuzhou, 350014, China. xrx_fch@163.com.
Abstract
PURPOSE: High-dose methotrexate (HD-MTX) is widely used in the treatment of non-Hodgkin lymphoma (NHL), but the pharmacokinetic properties of HD-MTX in Chinese adult patients with NHL have not yet been established through an approach that integrates genetic covariates. The purposes of this study were to identify both physiological and pharmacogenomic covariates that can explain the inter- and intraindividual pharmacokinetic variability of MTX in Chinese adult patients with NHL and to explore a new sampling strategy for predicting delayed MTX elimination. METHODS: A total of 852 MTX concentrations from 91 adult patients with NHL were analyzed using the nonlinear mixed-effects modeling method. FPGS, GGH, SLCO1B1, ABCB1 and MTHFR were genotyped using the Sequenom MassARRAY technology platform and were screened as covariates. The ability of different sampling strategies to predict the MTX concentration at 72 h was assessed through maximum a posteriori Bayesian forecasting using a validation dataset (18 patients). RESULTS: A two-compartment model adequately described the data, and the estimated mean MTX clearance (CL) was 6.03 L/h (9%). Creatinine clearance (CrCL) was identified as a covariate for CL, whereas the intercompartmental clearance (Q) was significantly affected by the body surface area (BSA). However, none of the genotypes exerted a significant effect on the pharmacokinetic properties of MTX. The percentage of patients with concentrations below 0.2 µmol/L at 72 h decreased from 65.6 to 42.6% when the CrCL decreased from 90 to 60 ml/min/1.73 m2 with a scheduled dosing of 3 g/m2, and the same trend was observed with dose regimens of 1 g/m2 and 2 g/m2. Bayesian forecasting using the MTX concentrations at 24 and 42 h provided the best predictive performance for estimating the MTX concentration at 72 h after dosing. CONCLUSIONS: The MTX population pharmacokinetic model developed in this study might provide useful information for establishing personalized therapy involving MTX for the treatment of adult patients with NHL.
PURPOSE: High-dose methotrexate (HD-MTX) is widely used in the treatment of non-Hodgkin lymphoma (NHL), but the pharmacokinetic properties of HD-MTX in Chinese adult patients with NHL have not yet been established through an approach that integrates genetic covariates. The purposes of this study were to identify both physiological and pharmacogenomic covariates that can explain the inter- and intraindividual pharmacokinetic variability of MTX in Chinese adult patients with NHL and to explore a new sampling strategy for predicting delayed MTX elimination. METHODS: A total of 852 MTX concentrations from 91 adult patients with NHL were analyzed using the nonlinear mixed-effects modeling method. FPGS, GGH, SLCO1B1, ABCB1 and MTHFR were genotyped using the Sequenom MassARRAY technology platform and were screened as covariates. The ability of different sampling strategies to predict the MTX concentration at 72 h was assessed through maximum a posteriori Bayesian forecasting using a validation dataset (18 patients). RESULTS: A two-compartment model adequately described the data, and the estimated mean MTX clearance (CL) was 6.03 L/h (9%). Creatinine clearance (CrCL) was identified as a covariate for CL, whereas the intercompartmental clearance (Q) was significantly affected by the body surface area (BSA). However, none of the genotypes exerted a significant effect on the pharmacokinetic properties of MTX. The percentage of patients with concentrations below 0.2 µmol/L at 72 h decreased from 65.6 to 42.6% when the CrCL decreased from 90 to 60 ml/min/1.73 m2 with a scheduled dosing of 3 g/m2, and the same trend was observed with dose regimens of 1 g/m2 and 2 g/m2. Bayesian forecasting using the MTX concentrations at 24 and 42 h provided the best predictive performance for estimating the MTX concentration at 72 h after dosing. CONCLUSIONS: The MTX population pharmacokinetic model developed in this study might provide useful information for establishing personalized therapy involving MTX for the treatment of adult patients with NHL.
Entities:
Keywords:
Methotrexate; NONMEM; Non-Hodgkin lymphoma; Pharmacogenomics; Population pharmacokinetics
Authors: Marc Ghannoum; Darren M Roberts; David S Goldfarb; Jesper Heldrup; Kurt Anseeuw; Tais F Galvao; Thomas D Nolin; Robert S Hoffman; Valery Lavergne; Paul Meyers; Sophie Gosselin; Tudor Botnaru; Karine Mardini; David M Wood Journal: Clin J Am Soc Nephrol Date: 2022-03-02 Impact factor: 10.614