Evidence map›Paper›PMID 42243443›Full record

ArticleClinical pharmacokinetics2026

A Model-Informed Early Prediction of Methotrexate-Induced Acute Kidney Injury in Pediatric Patients with Osteosarcoma Using Real-World Data: A Multi-center Pharmacokinetic Study.

Patricia Rega, Jorge Morales Vallespin, Azucena Aldaz, Andrew Pappas, Paulo Caceres Guido, Manuel Azocar, Milena Villarroel, Jennifer L Pauley, Clinton F Stewart, Manuel Ibarra and 1 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Clinical pharmacokinetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Patricia RegaDepartment of Pharmaceutical Sciences, Faculty of Chemistry, Universidad de la República, Montevideo, Uruguay.ORCID http://orcid.org/0009-0002-4474-6077
Jorge Morales VallespinDepartment of Pharmacy, Hospital Calvo Mackenna, Santiago de Chile, Chile.ORCID http://orcid.org/0000-0003-3363-7262
Azucena AldazDepartment of Pharmacy, Clinica Universidad de Navarra, Navarra, Spain.ORCID http://orcid.org/0000-0002-3810-6025
Andrew PappasDepartment of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, USA.
Paulo Caceres GuidoUnit of Clinical Pharmacokinetics, Hospital de Pediatria JP Garrahan, Buenos Aires, Argentina.ORCID http://orcid.org/0000-0002-9747-4960
Manuel AzocarDepartment of Pharmacy, Hospital Calvo Mackenna, Santiago de Chile, Chile.
Milena VillarroelOncology Service, Hospital Calvo Mackenna, Santiago de Chile, Chile.
Jennifer L PauleyDepartment of Global Pediatric Medicine, St. Jude Children's Research Hospital, Memphis, TN, USA.ORCID http://orcid.org/0009-0006-8811-1718
Clinton F Stewart *Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, USA.ORCID http://orcid.org/0000-0002-1546-9720
Manuel Ibarra *Department of Pharmaceutical Sciences, Faculty of Chemistry, Universidad de la República, Montevideo, Uruguay.ORCID http://orcid.org/0000-0002-0484-6367
Paula Schaiquevich *Unit of Innovative Treatments, Hospital de Pediatria JP Garrahan, Combate de los Pozos 1881, CP1245, CABA, Buenos Aires, Argentina. paulas@conicet.gov.ar.ORCID http://orcid.org/0000-0002-2568-4731

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveHigh-dose methotrexate is central to pediatric osteosarcoma treatment, but delayed elimination increases the risk of acute kidney injury, compromising treatment intensity. Current therapeutic drug monitoring often identifies toxicity too late for intervention. This study aimed to develop a population pharmacokinetic model using real-world data to identify early predictors of methotrexate-associated acute kidney injury.

methodsWe conducted a multicenter study including 248 pediatric, adolescent, and young adult patients with osteosarcoma (1809 high-dose methotrexate courses) from referral centers in Argentina, Chile, Spain, and the USA. A population pharmacokinetic model was developed using Monolix. Early predictors of acute kidney injury were assessed using receiver operating characteristic curve analysis, and optimal sampling times earlier than those currently used in the clinics for rescue measures were explored through simulation.

resultsA two-compartment population pharmacokinetic model adequately described the data up to 48 h after the start of infusion. Time-varying serum creatinine was included as a key covariate for methotrexate clearance. The alpha disposition half-life was the most accurate early predictor of moderate-to-severe acute kidney injury (area under the curve-receiver operating characteristic: 0.956 (95% confidence interval (CI) 0.914-0.998); threshold: 3.4 h (95% confidence interval 2.7-3.7), sensitivity: 0.94 [95% CI 0.81-1.0], and specificity: 0.96 [95% CI 0.73-0.99]). A two-point early sampling strategy (C4 and C8-11) precisely estimated alpha disposition half-life.

conclusionsEarly identification of impaired methotrexate clearance is feasible using model-based metrics available within the first 11 h after the end of infusion by means of the alpha disposition half-life. This approach may guide supportive care offering a practical tool for model-informed precision dosing in pediatric patients with osteosarcoma.

Indexed as

Acute Kidney InjuryAntimetabolites, AntineoplasticBone NeoplasmsMethotrexateModels, BiologicalOsteosarcomaAdolescentChildChild, PreschoolCreatinineFemaleHumansMaleYoung AdultAntimetabolites, AntineoplasticCreatinineMethotrexate

Identifiers

What Socratic 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 Socratic graph.