Population pharmacokinetics of methotrexate in paediatric patients with acute lymphoblastic leukaemia and malignant lymphoma

Zhan, Min; Sun, Yiqi; Zhou, Fang; Wang, Honghong; Chen, Zebin; Yan, Lianzhen; Li, Xingang

doi:10.1080/00498254.2022.2069060

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…The literature search identified a total of fourteen papers detailing the population pharmacokinetics of MTX in children [ 11 , 18 , 19 , 20 , 21 , 26 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Among them, six studies included ALL patients greater than 13 years or infants and were excluded for further analysis [ 18 , 31 , 32 , 33 , 34 , 35 , 37 ]. In addition, one PopPK study was conducted on pediatric patients with osteosarcoma and was also excluded [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

External Evaluation of Population Pharmacokinetic Models of Methotrexate for Model-Informed Precision Dosing in Pediatric Patients with Acute Lymphoid Leukemia

et al. 2023

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Background: Methotrexate (MTX) is a key immunosuppressant for children with acute lymphoid leukemia (ALL), and it has a narrow therapeutic window and relatively high pharmacokinetic variability. Several population pharmacokinetic (PopPK) models of MTX in ALL children have been reported, but the validity of these models for model-informed precision dosing in clinical practice is unclear. This study set out to evaluate the predictive performance of published pediatric PopPK models of MTX using an independent patient cohort. Methods: A PubMed literature search was performed to identify suitable models for evaluation. Demographics and measurements of the validation dataset were retrospectively collected from the medical records of ALL children who had received intravenous MTX. Predictive performance for each model was assessed by visual comparison of predictions to observations, median and mean predicted error (PE), and relative root mean squared error (RMSE). Results: Six models were identified for external evaluation, carried out on a dataset containing 354 concentrations from 51 pediatrics. Model performance varied considerably from one model to another. Different models had the median PE for population and individual predictions at −33.23% to 442.04% and −25.20% to 6.52%, mean PE for population and individual predictions at −25.51% to 780.87% and 1.33% to 64.44%, and RMSE for population and individual predictions at 62.88% to 1182.24% and 63.39% to 152.25%. All models showed relatively high RMSE. Conclusions: Some of the published models showed reasonably low levels of bias but had some problems with imprecision, and extensive evaluation is needed before model application in clinical practice.

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Section: Resultsmentioning

confidence: 99%

External Evaluation of Population Pharmacokinetic Models of Methotrexate for Model-Informed Precision Dosing in Pediatric Patients with Acute Lymphoid Leukemia

et al. 2023

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“…Likewise, popPK approaches are well established and have shown ability to characterize and predict exposures in pediatric patients by accounting for body size covariates for pediatric age ranges associated with maturation of physiological disposition processes. Indeed, popPK models with allometrically scaled body size covariates have successfully captured/predicted pediatric data for the anticancer agents ponatinib, 37 entrectinib, 38 and methotrexate 39 . Our example leverages the strengths of the complimentary modeling tools through incorporation of PBPK modeling accounting for maturation of copanlisib disposition properties to predict pediatric doses/exposures prior to study initiation and popPK modeling, which utilizes an established adult model to descriptively characterize and estimate exposures from emerging pediatric clinical PK data.…”

Section: Discussionmentioning

confidence: 99%

Model‐informed approach to support pediatric dosing for the pan‐PI3K inhibitor copanlisib in children and adolescents with relapsed/refractory solid tumors

Morcos

Schlender

Burghaus

et al. 2023

Clinical Translational Sci

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Copanlisib is an intravenously administered phosphatidylinositol 3-kinase (PI3K) inhibitor which was investigated in pediatric patients with relapsed/refractory solid tumors. A model-informed approach was undertaken to support and confirm an empirically selected starting dose of 28 mg/m 2 for pediatric pa-How to cite this article: Morcos PN, Schlender J, Burghaus R, et al. Model-informed approach to support pediatric dosing for the pan-PI3K inhibitor copanlisib in children and adolescents with relapsed/refractory solid tumors.

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“…Results on [11][12][13] show evidence for the contribution pharmacogenetics to the toxicity of highdose MTX and plasma MTX concentrations 48 hours after treatment in patients with ALL or non-Hodgkin lymphoma. The influence of MTHFR C677T on MTX-related toxicities and plasma levels were confirmed.…”

Section: Of 13mentioning

confidence: 96%

Detecting Methotrexate in Pediatric Patients Using Artificial Neural Networks

Santiago,

Torres,

Rodríguez

et al. 2024

Preprint

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Methotrexate is an antimetabolic agent with proliferative and immunosuppressive activity. It has been demonstrated to be an effective treatment for acute lymphoblastic leukemia (ALL) in children. However, there is evidence of an association between methotrexate and toxicity risks, which influences the personalization of treatment, particularly in the case of childhood ALL. This article presents the development and implementation of an algorithm based on artificial neural networks to detect methotrexate toxicity in pediatric patients with acute lymphoblastic leukemia. The algorithm utilizes historical clinical and laboratory data, with an effectiveness of 99% in the tests performed with the patients dataset. The use of neural networks in medicine is often linked to disease diagnosis systems. However, neural networks are not only capable of recognizing examples, but also hold very important information. For this reason, one of the main areas of application of neural networks is the interpretation of medical data. In this article we diagnose with the application of neural networks in medicine with a concrete example: Detecting Methotrexate in Pediatric Patient in its early stages.

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Population pharmacokinetics of methotrexate in paediatric patients with acute lymphoblastic leukaemia and malignant lymphoma

Cited by 6 publications

References 52 publications

External Evaluation of Population Pharmacokinetic Models of Methotrexate for Model-Informed Precision Dosing in Pediatric Patients with Acute Lymphoid Leukemia

External Evaluation of Population Pharmacokinetic Models of Methotrexate for Model-Informed Precision Dosing in Pediatric Patients with Acute Lymphoid Leukemia

Model‐informed approach to support pediatric dosing for the pan‐PI3K inhibitor copanlisib in children and adolescents with relapsed/refractory solid tumors

Detecting Methotrexate in Pediatric Patients Using Artificial Neural Networks

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