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• W3132230083 abstract "The aim of this study was to develop a novel busulfan dosing regimen, based on a population pharmacokinetic (PPK) model in Chinese children, and to achieve better area under the concentration-time curve (AUC) targeting.We collected busulfan concentration-time samples from 69 children who received intravenous busulfan prior to allogeneic hematopoietic stem cell transplantation (allo-HSCT). A population pharmacokinetic model for busulfan was developed by nonlinear mixed effect modelling and was validated by an external dataset (n=14). A novel busulfan dosing regimen was developed through simulated patients, and has been verified on real patients. Limited sampling strategy (LSS) was established by Bayesian forecasting. Mean absolute prediction error (MAPE) and relative root mean Squared error (rRMSE) were calculated to evaluate predictive accuracy.A one-compartment model with first-order elimination best described the data. GSTA1 genotypes, body surface area (BSA) and aspartate aminotransferase (AST) were found to be significant covariates of Bu clearance, and BSA had significant impact of the volume of distribution. Moreover, two equations were obtained for recommended dose regimens: dose (mg)=34.14×BSA (m2)+3.75 (for GSTA1 *A/*A), Dose (mg)=30.99×BSA (m2)+3.21 (for GSTA1 *A/*B). We also presented a piecewise dosage based on BSA categories for each GSTA1 mutation. A two-point LSS, two hours and four hours after dosing, behaved well with acceptable prediction precision (rRMSE=1.026%, MAPE=6.55%).We recommend a GSTA1-BSA and BSA-based dosing (Q6 h) based on a PPK model for personalizing busulfan therapy in pediatric population. Additionally, an optimal LSS (C2h and C4h) provides convenience for therapeutic drug monitoring (TDM) in the future." @default.
• W3132230083 created "2021-03-01" @default.
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• W3132230083 date "2021-02-01" @default.
• W3132230083 modified "2023-09-26" @default.
• W3132230083 title "Optimization of Busulfan Dosing Regimen in Pediatric Patients Using a Population Pharmacokinetic Model Incorporating GST Mutations" @default.
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• W3132230083 doi "https://doi.org/10.2147/pgpm.s289834" @default.
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