Optimal teicoplanin dosing regimen in neonates and children developed by leveraging real-world clinical information

Ther Drug Monit. 2021 Oct 8. doi: 10.1097/FTD.0000000000000930. Online ahead of print.


BACKGROUND: Teicoplanin is a glycopeptide antibiotic used for the treatment of methicillin-resistant Staphylococcus aureus infections. To ensure successful target attainment, therapeutic drug monitoring-informed dosage adjustment is recommended. However, it relies on the experience of the clinician and the frequency of drug measurements. This study aimed to design a new optimal dosing regimen of teicoplanin with a maintenance dosing strategy for neonates and children based on their physiological characteristics.

METHODS: Data from teicoplanin-treated patients (n = 214) were collected from electronic medical records. Covariate analyses were performed using population pharmacokinetic (PK) modeling with 399 serum teicoplanin concentrations from 48 neonates and 166 children. Multiple PK simulations were conducted to explore optimal dosing regimens that would allow control of the trough concentration to the target of 15-30 mg/L quicker than the current standard regimen.

RESULTS: Allometrically scaled body weight, postmenstrual age (PMA), renal function, and serum albumin were implemented as substantial covariates for teicoplanin clearance in a two-compartment PK model. Covariate analyses and comprehensive simulation assessments recommended the following modifications to the current regimen: 1) decreased dose for premature babies (PMA ≤ 28 weeks), 2) decreased dose for children with renal dysfunction, and 3) increased dose for children (0.5-11 years) with an estimated glomerular filtration rate of ≥90 mL/min/1.73 m2.

CONCLUSIONS: This study leverages real-world clinical information and proposes new optimal dosing regimens for teicoplanin in neonates and children through PK modeling and simulation analyses, taking into account the age, including PMA, and renal function of patients.

PMID:34629445 | DOI:10.1097/FTD.0000000000000930