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Population pharmacokinetics of ciprofloxacin in newborns with early onset neonatal sepsis and suspected meningitis

  • Kunal Garg
  • , Ritika Kondel Bhandari
  • , Nusrat Shafiq
  • , Suksham Jain
  • , Shivani Jaswal
  • , Deepak Chawla
  • , Surulivelrajan Mallayasamy
  • , Supreet Khurana*
  • , Jaya Shree Dilli Batcha
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Neonatal Sepsis accounts for significant proportion of neonatal mortality globally. Ciprofloxacin can be used as an effective antimicrobial against common causative agents of neonatal sepsis. However, there is only limited information about its pharmacokinetic distribution in plasma and Cerebrospinal fluid (CSF) of neonates. Methods: Plasma and CSF samples were taken using a sparse sampling technique from neonates who received at least one dose of intravenous ciprofloxacin. Ciprofloxacin levels were analysed using high-performance liquid chromatography (HPLC). Population pharmacokinetic analysis was conducted using a non-linear mixed-effects modelling using Pumas® (Pharmaceutical Modelling and Simulation) package (Version 2.0). Results: 53 neonates were enroled in the study of whom; 9 (17%) had meningitis. The median concentration of ciprofloxacin in CSF was 1.4 (0.94–2.06) ug/ml and plasma was 2.94 (1.8–5.0) ug/ml. A one-compartment model with first-order elimination fitted the data. Body weight was found to be a significant covariate on volume of distribution (Vd). Simulations based on the final model suggest that dose of 10 mg/kg, intravenous b.d may not be able to achieve the desirable indices. Conclusions: One compartment model with weight as a covariate explained the available data. Further studies with modified sampling strategy, larger sample size and variable dose levels are needed.

Original languageEnglish
Pages (from-to)1273-1278
Number of pages6
JournalPediatric Research
Volume95
Issue number5
DOIs
Publication statusAccepted/In press - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • Pediatrics, Perinatology, and Child Health

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