Population pharmacokinetics and dosing optimization of azlocillin in neonates with early-onset sepsis: a real-world study

Abstract

Abstract Objectives Nowadays, real-world data can be used to improve currently available dosing guidelines and to support regulatory approval of drugs for use in neonates by overcoming practical and ethical hurdles. This proof-of-concept study aimed to assess the population pharmacokinetics of azlocillin in neonates using real-world data, to make subsequent dose recommendations and to test these in neonates with early-onset sepsis (EOS). Methods This prospective, open-label, investigator-initiated study of azlocillin in neonates with EOS was conducted using an adaptive two-step design. First, a maturational pharmacokinetic-pharmacodynamic model of azlocillin was developed, using an empirical dosing regimen combined with opportunistic samples resulting from waste material. Second, a Phase II clinical trial (ClinicalTrials.gov: NCT03932123) of this newly developed model-based dosing regimen of azlocillin was conducted to assure optimized target attainment [free drug concentration above MIC during 70% of the dosing interval (‘70% fT>MIC’)] and to investigate the tolerance and safety in neonates. Results A one-compartment model with first-order elimination, using 167 azlocillin concentrations from 95 neonates (31.7–41.6 weeks postmenstrual age), incorporating current weight and renal maturation, fitted the data best. For the second step, 45 neonates (30.3–41.3 weeks postmenstrual age) were subsequently included to investigate target attainment, tolerance and safety of the pharmacokinetic-pharmacodynamic model-based dose regimen (100 mg/kg q8h). Forty-three (95.6%) neonates reached their pharmacokinetic target and only two neonates experienced adverse events (feeding intolerance and abnormal liver function), possibly related to azlocillin. Conclusions Target attainment, tolerance and safety of azlocillin was shown in neonates with EOS using a pharmacokinetic-pharmacodynamic model developed with real-world data.