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

• Published in: Pharmaceutics 2023-02, Vol.15 (2), p.569
• Main Authors: Wang, Shengfeng, Yin, Qiufen, Yang, Minghua, Cheng, Zeneng, Xie, Feifan
• Format: Article
• Language: English
• Subjects: Acute lymphocytic leukemia; acute lymphoid leukemia; Age; children; Datasets; Demographic aspects; Dosage and administration; Drug dosages; Drug therapy; Evaluation; external validation; Leukemia; Methotrexate; Patients; Pharmacokinetics; population pharmacokinetics; Testing; Toxicity
• ISSN: 1999-4923; 1999-4923
• DOI: 10.3390/pharmaceutics15020569

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. 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). 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. 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.