Population pharmacokinetics of piperacillin based on multicenter data to support model-informed precision dosing in critically ill women
8 affiliations
- 1Department of Anaesthesiology, LMU University Hospital, LMU Munich, Munich, Germany
- 2Department of Anaesthesiology and Intensive Care Medicine, General Hospital of Heidenheim, Heidenheim, Germany
- 3Department of Pharmacy, General Hospital of Heidenheim, Heidenheim, Germany
- 4Laboratory Dr. Brunner, Laboratory Medical Care Center Konstanz GmbH, Constance, Germany
- 5Institute for Infectious Diseases and Infection Control, Jena University Hospital, Friedrich‐Schiller‐University Jena, Jena, Germany
- 6Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
- 7Department of Pharmaceutical and Medicinal Chemistry I, University of Regensburg, Regensburg, Germany
- 8Doctoral Program Clinical Pharmacy, LMU University Hospital, LMU Munich, Munich, Germany
Abstract
Sex-specific differences in pharmacokinetics are increasingly recognized as clinically relevant, yet most population pharmacokinetic models for antibiotics have been developed in predominantly male populations with limited transferability to female patients. This study aimed to develop and externally evaluate a population pharmacokinetic model of piperacillin specifically for critically ill women to support model-informed precision dosing (MIPD). A total of 923 samples from 165 critically ill women (from four previously published studies) receiving piperacillin/tazobactam were analyzed. The data set was randomly split into a training set (80%) and an independent external evaluation set (20%). Non-linear mixed-effects modeling was employed based on the training data using NONMEM, and covariates were identified using stepwise covariate modeling with cross-validation. Prediction accuracy and bias were quantified using the median prediction error (MDPE) and the median absolute prediction error (MDAPE) in an MIPD scenario. Model predictions for future concentrations were generated both a priori and through Bayesian forecasting based on one prior sample. A two-compartment model provided the best fit for the data. Clearance was significantly affected by renal function estimated by the Cockcroft–Gault equation, along with procalcitonin and serum albumin. The external evaluation demonstrated strong predictive performance with very low systematic bias. In the MIPD scenario, a priori predictions exhibited good predictive performance (MDPE < |5.2|%, MDAPE < 33.4%), which substantially improved with Bayesian forecasting (MDPE < |3.2|%, MDAPE < 19.1%). A female-specific population pharmacokinetic model of piperacillin was developed and externally validated, demonstrating robust predictive performance and clear benefits for Bayesian forecasting.
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BibTeX
@article{greppmair2026population,
title = {{Population pharmacokinetics of piperacillin based on multicenter data to support model-informed precision dosing in critically ill women}},
author = {Sebastian Greppmair and Sina Pesthy and Michael Zoller and Christina Scharf and Alexander Brinkmann and Otto Frey and Johannes Zander and Nadine Liebchen and Stefan Hagel and Sebastian G. Wicha and Christoph Dorn and Sabrina Böing and Uwe Liebchen},
journal = {Antimicrobial Agents and Chemotherapy},
year = {2026},
month = oct,
doi = {10.1128/aac.00986-26},
url = {https://doi.org/10.1128/aac.00986-26},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Population pharmacokinetics of piperacillin based on multicenter data to support model-informed precision dosing in critically ill women
AU - Sebastian Greppmair
AU - Sina Pesthy
AU - Michael Zoller
AU - Christina Scharf
AU - Alexander Brinkmann
AU - Otto Frey
AU - Johannes Zander
AU - Nadine Liebchen
AU - Stefan Hagel
AU - Sebastian G. Wicha
AU - Christoph Dorn
AU - Sabrina Böing
AU - Uwe Liebchen
PY - 2026
DA - 2026/10/14
JO - Antimicrobial Agents and Chemotherapy
DO - 10.1128/aac.00986-26
UR - https://doi.org/10.1128/aac.00986-26
AB - Sex-specific differences in pharmacokinetics are increasingly recognized as clinically relevant, yet most population pharmacokinetic models for antibiotics have been developed in predominantly male populations with limited transferability to female patients. This study aimed to develop and externally evaluate a population pharmacokinetic model of piperacillin specifically for critically ill women to support model-informed precision dosing (MIPD). A total of 923 samples from 165 critically ill women (from four previously published studies) receiving piperacillin/tazobactam were analyzed. The data set was randomly split into a training set (80%) and an independent external evaluation set (20%). Non-linear mixed-effects modeling was employed based on the training data using NONMEM, and covariates were identified using stepwise covariate modeling with cross-validation. Prediction accuracy and bias were quantified using the median prediction error (MDPE) and the median absolute prediction error (MDAPE) in an MIPD scenario. Model predictions for future concentrations were generated both a priori and through Bayesian forecasting based on one prior sample. A two-compartment model provided the best fit for the data. Clearance was significantly affected by renal function estimated by the Cockcroft–Gault equation, along with procalcitonin and serum albumin. The external evaluation demonstrated strong predictive performance with very low systematic bias. In the MIPD scenario, a priori predictions exhibited good predictive performance (MDPE < |5.2|%, MDAPE < 33.4%), which substantially improved with Bayesian forecasting (MDPE < |3.2|%, MDAPE < 19.1%). A female-specific population pharmacokinetic model of piperacillin was developed and externally validated, demonstrating robust predictive performance and clear benefits for Bayesian forecasting.
SN - 0066-4804
SN - 1098-6596
ER -