A population pharmacokinetic model of polymyxin B based on prospective clinical data to inform dosing in hospitalised patients. Academic Article uri icon

Overview

abstract

  • OBJECTIVES: To develop a population pharmacokinetic (PK) model with data from the largest polymyxin B-treated patient population studied to date to optimise its dosing in hospitalised patients. METHODS: Hospitalised patients receiving intravenous polymyxin B for ≥48h were enrolled. Blood samples were collected at steady-state and drug concentrations were analysed by LC-MS/MS. Population PK analysis and Monte Carlo simulations were performed to determine the probability of target attainment (PTA). RESULTS: One hundred and forty-two patients received intravenous polymyxin B (1.33-6 mg/kg/day), providing 681 plasma samples. Twenty-four patients were on renal replacement therapy (RRT), including 13 on continuous veno-venous hemodiafiltration (CVVHDF). A two-compartment model adequately described the PK with body weight as a covariate on volume of distribution that affected Cmax, but it did not impact clearance or exposure. Creatinine clearance (CrCL) was a statistically significant covariate on clearance, although clinically relevant variations of dose-normalized drug exposure were not observed across a wide CrCL range. The model described higher clearance in CVVHDF patients than in non-CVVHDF patients. Maintenance doses of ≥2.5 mg/kg/day or ≥150 mg/day had a PTA≥90% (for non-pulmonary infections target) at steady state for MICs≤2mg/L. The PTA at steady state for CVVHDF patients was lower. CONCLUSIONS: Fixed loading and maintenance doses of polymyxin B seemed to be more appropriate than weight-based dosing regimens in patients weighing 45-90kg. Higher doses may be needed in patients on CVVHDF. Substantial variability in polymyxin B clearance and volume of distribution was found, suggesting that therapeutic drug monitoring may be indicated.

publication date

  • May 20, 2023

Research

keywords

  • Hemodiafiltration
  • Polymyxin B

Identity

Digital Object Identifier (DOI)

  • 10.1016/j.cmi.2023.05.018

PubMed ID

  • 37217076