Evidence map›Paper›PMID 34985725›Full record

SynthesisEuropean journal of drug metabolism and pharmacokinetics2022

A Systematic Review of Population Pharmacokinetic Models of Methotrexate.

Yiming Zhang, Liyu Sun, Xinwei Chen, Libo Zhao, Xiaoling Wang, Zhigang Zhao, Shenghui Mei

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in European journal of drug metabolism and pharmacokinetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
5.2field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
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  5. Children (Basel, Switzerland) · 2025
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Yiming Zhang *Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, 119 Nansihuan West Road, Fengtai District, Beijing, 100070, People's Republic of China.
Liyu Sun *Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, 119 Nansihuan West Road, Fengtai District, Beijing, 100070, People's Republic of China.
Xinwei ChenDepartment of Clinical Pharmacology, College of Pharmaceutical Sciences, Capital Medical University, Beijing, 100069, People's Republic of China.
Libo ZhaoDepartment of Clinical Pharmacology, College of Pharmaceutical Sciences, Capital Medical University, Beijing, 100069, People's Republic of China.
Xiaoling WangDepartment of Clinical Pharmacology, College of Pharmaceutical Sciences, Capital Medical University, Beijing, 100069, People's Republic of China.
Zhigang ZhaoDepartment of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, 119 Nansihuan West Road, Fengtai District, Beijing, 100070, People's Republic of China. ttyyzzg1022@126.com.
Shenghui MeiDepartment of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, 119 Nansihuan West Road, Fengtai District, Beijing, 100070, People's Republic of China. meishenghui1983@126.com.ORCID http://orcid.org/0000-0003-1385-4315
Capital Medical University · CN

Funding

beijing municipal administration of hospitals ZYLX201827beijing municipal health bureau 2018000021469G238national key r&d program of china 2020YFC2008306
6 · The paper itself

Abstract

BACKGROUND AND

objectivesMethotrexate (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.

methodsWe 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.

resultsThirty-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.

conclusionsVarious 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.

Indexed as

Antimetabolites, AntineoplasticMethotrexateBody Surface AreaHumansLiver-Specific Organic Anion Transporter 1Models, BiologicalPolymorphism, GeneticAntimetabolites, AntineoplasticLiver-Specific Organic Anion Transporter 1MethotrexateSLCO1B1 protein, human

Identifiers

PMID34985725
OpenAlexW4206632777

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.