Population Pharmacokinetics and Individualized Medication of Azithromycin for Injection in Children Under 6 Years Old

Pharmacokinetic data for injectable azithromycin in children remain limited. This study aims to develop and validate a population pharmacokinetic model of azithromycin for injection in children under 6 years old and optimize its dosage regimen in this population. We prospectively enrolled patients u...

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Veröffentlicht in:Journal of pharmaceutical sciences 2024-05, Vol.113 (5), p.1351-1358
Hauptverfasser: Zhang, Yuwen, Wang, Yinghui, Li, Jiangshuo, Wang, Honghong, Hou, Mengyu, Dong, Ruihua, Li, Xingang
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Sprache:eng
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Zusammenfassung:Pharmacokinetic data for injectable azithromycin in children remain limited. This study aims to develop and validate a population pharmacokinetic model of azithromycin for injection in children under 6 years old and optimize its dosage regimen in this population. We prospectively enrolled patients under 6 years old who received azithromycin for injection at Beijing Friendship Hospital, Capital Medical University. Demographic information, clinical characteristics, and venous blood samples were collected in accordance with the research protocol. Azithromycin concentrations were determined using a validated UPLC-MS/MS method. The population pharmacokinetic model was structured using Phoenix NLME. The adequacy and robustness of the model was evaluated using VPC and bootstrap. We optimized azithromycin's dosing regimen for injection through Monte Carlo simulations. We included 254 plasma concentration data from 148 patients to establish the model. The clearance and volume were 1.27 L/h/kg and 45.6 L/kg, respectively. The covariates included were weight and age. VPC plots and nonparametric bootstrap showed that the final PPK model was reliable and robust. Based on Monte Carlo simulation, we derived a simple and practical dosing scheme. The results provided reference for individualized dosing in this population. The individualized dosing scheme based on Monte Carlo simulation can optimize clinical decision-making and guide personalized therapy.
ISSN:0022-3549
1520-6017
DOI:10.1016/j.xphs.2024.01.012