Yiming Zhang1,2, Liyu Sun1,2, Xinwei Chen2,3, Libo Zhao2,3, Xiaoling Wang2,3, Zhigang Zhao4,5, Shenghui Mei6,7. 1. Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, 119 Nansihuan West Road, Fengtai District, Beijing, 100070, People's Republic of China. 2. Department of Clinical Pharmacology, College of Pharmaceutical Sciences, Capital Medical University, Beijing, 100069, People's Republic of China. 3. Department of Pharmacy, Beijing Children's Hospital, National Center for Children's Health, Capital Medical University, Beijing, 100045, People's Republic of China. 4. Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, 119 Nansihuan West Road, Fengtai District, Beijing, 100070, People's Republic of China. ttyyzzg1022@126.com. 5. Department of Clinical Pharmacology, College of Pharmaceutical Sciences, Capital Medical University, Beijing, 100069, People's Republic of China. ttyyzzg1022@126.com. 6. Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, 119 Nansihuan West Road, Fengtai District, Beijing, 100070, People's Republic of China. meishenghui1983@126.com. 7. Department of Clinical Pharmacology, College of Pharmaceutical Sciences, Capital Medical University, Beijing, 100069, People's Republic of China. meishenghui1983@126.com.
Abstract
BACKGROUND AND OBJECTIVES: Methotrexate (MTX) is widely used for the treatment of a variety of neoplastic and autoimmune diseases. However, its toxicity and efficacy varied greatly among individuals, and they could be predicted by its pharmacokinetics. Many population pharmacokinetic models have been published to describe MTX pharmacokinetics. The objective of this systematic review was to summarize and discuss covariates with significant influence on MTX pharmacokinetics. METHODS: We searched PubMed and EMBASE databases from their inception to April 2021 for population pharmacokinetic of MTX. The articles were screened by inclusion and exclusion criteria. The characteristics of studies and information for model construction and validation were extracted, summarized and discussed. RESULTS: Thirty-five articles were included. The two-compartment model well described the pharmacokinetic behavior of MTX. For inter-individual variability, an exponential distribution error model was usually used for high-dose MTX population pharmacokinetic models, while a proportional distribution error model was used for low-dose MTX population pharmacokinetic models. Proportional and combined proportional and additive error models were used to describe residual error. Renal function was an independent indicator of MTX clearance. Body weight, age, gene polymorphisms (SLCO1B1, ABCC2, ABCB1, ABCG2 and MTHFR) and co-medications (proton pump inhibitors, non-steroidal anti-inflammatory drug, dexamethasone, vancomycin, penicillin and salicylic acid) could influence MTX clearance. Body weight, body surface area, age and dosage regimen have significant influence on MTX central compartment volume. Internal bootstrap test, external validation and visual predictive check were used to evaluate model predictive ability. CONCLUSIONS: Various covariates could affect MTX pharmacokinetics, and their relationships have been summarized and discussed. This review will be helpful for researchers to develop their own population pharmacokinetic models and select appropriate models for individualized therapy of MTX.
BACKGROUND AND OBJECTIVES: Methotrexate (MTX) is widely used for the treatment of a variety of neoplastic and autoimmune diseases. However, its toxicity and efficacy varied greatly among individuals, and they could be predicted by its pharmacokinetics. Many population pharmacokinetic models have been published to describe MTX pharmacokinetics. The objective of this systematic review was to summarize and discuss covariates with significant influence on MTX pharmacokinetics. METHODS: We searched PubMed and EMBASE databases from their inception to April 2021 for population pharmacokinetic of MTX. The articles were screened by inclusion and exclusion criteria. The characteristics of studies and information for model construction and validation were extracted, summarized and discussed. RESULTS: Thirty-five articles were included. The two-compartment model well described the pharmacokinetic behavior of MTX. For inter-individual variability, an exponential distribution error model was usually used for high-dose MTX population pharmacokinetic models, while a proportional distribution error model was used for low-dose MTX population pharmacokinetic models. Proportional and combined proportional and additive error models were used to describe residual error. Renal function was an independent indicator of MTX clearance. Body weight, age, gene polymorphisms (SLCO1B1, ABCC2, ABCB1, ABCG2 and MTHFR) and co-medications (proton pump inhibitors, non-steroidal anti-inflammatory drug, dexamethasone, vancomycin, penicillin and salicylic acid) could influence MTX clearance. Body weight, body surface area, age and dosage regimen have significant influence on MTX central compartment volume. Internal bootstrap test, external validation and visual predictive check were used to evaluate model predictive ability. CONCLUSIONS: Various covariates could affect MTX pharmacokinetics, and their relationships have been summarized and discussed. This review will be helpful for researchers to develop their own population pharmacokinetic models and select appropriate models for individualized therapy of MTX.
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