Physiologically-based pharmacokinetic modelling in sepsis: A tool to elucidate how pathophysiology affects meropenem pharmacokinetics

被引:0
|
作者
Bahnasawy, Salma M. [1 ]
Parrott, Neil J. [2 ]
Gijsen, Matthias [3 ,4 ]
Spriet, Isabel [3 ,4 ]
Friberg, Lena E. [1 ]
Nielsen, Elisabet I. [1 ]
机构
[1] Uppsala Univ, Dept Pharm, Box 580, S-75123 Uppsala, Sweden
[2] Roche Innovat Ctr Basel, Pharmaceut Sci, Roche Pharm Res & Early Dev, Basel, Switzerland
[3] Katholieke Univ Leuven, Dept Pharmaceut & Pharmacol Sci, Leuven, Belgium
[4] Univ Hosp Leuven, Pharm Dept, Leuven, Belgium
关键词
Sepsis; PBPK; Meropenem; CRITICALLY-ILL PATIENTS; PROTEIN-BINDING; SEPTIC PATIENTS; DEFINITIONS; VANCOMYCIN; METABOLITE; CLEARANCE; IMIPENEM; PREDICT; DRUGS;
D O I
10.1016/j.ijantimicag.2024.107352
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
Objectives: Applying physiologically-based pharmacokinetic (PBPK) modelling in sepsis could help to better understand how PK changes are influenced by drug- and patient-related factors. We aimed to elucidate the influence of sepsis pathophysiology on the PK of meropenem by applying PBPK modelling. Methods: A whole-body meropenem PBPK model was developed and evaluated in healthy individuals, and renally impaired non-septic patients. Sepsis-induced physiological changes in body composition, organ blood flow, kidney function, albumin, and haematocrit were implemented according to a previously proposed PBPK sepsis model. Model performance was evaluated, and a local sensitivity analysis was conducted. Results: The model-predicted PK metrics (AUC, C max , CL, V ss ) were within 1.33-fold-error margin of published data for 87.5% of the simulated profiles in healthy individuals. In sepsis, the model provided good predictions for literature-digitised average plasma and tissue exposure data, where the model-predicted AUC was within 1.33-fold-error margin for 9 out 11 simulated study profiles. Furthermore, the model was applied to individual plasma concentration data from 52 septic patients, where the model-predicted AUC, C max , and CL had a fold-error ratio range of 0.98-1.12, with alignment of the predicted and observed variability. For V ss , the fold-error ratio was 0.81, and the model underpredicted the population variability. CL was sensitive to renal plasma clearance, and kidney volume, whereas V ss was sensitive to the unbound fraction, organ volume fraction of the interstitial compartment, and the organ volume. Conclusions: These findings may be extended to more diverse drug types and support a more mechanistic understanding of the effect of sepsis on drug exposure. (c) 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Physiologically-based pharmacokinetic modelling of inter-individual variability in chemical toxicity
    Loizou, GD
    Spendiff, M
    TOXICOLOGY, 2003, 192 (01) : 78 - 79
  • [22] THE EXPOSURE TO AND EFFICACY OF DORAVIRINE IN PREGNANT WOMEN AS ASSESSED BY PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELLING
    van Hove, Hedwig
    Bukkems, Vera
    Roelofsen, Damian
    Freriksen, Jolien
    van Drongelen, Joris
    Svensson, Elin
    Colbers, Angela
    Greupink, Rick
    ARCHIVES OF DISEASE IN CHILDHOOD, 2023, 108 (06) : A1 - A1
  • [23] Physiologically-based pharmacokinetic modelling and dosing evaluation of gentamicin in neonates using PhysPK
    Zazo, Hinojal
    Lagarejos, Eduardo
    Prado-Velasco, Manuel
    Sanchez-Herrero, Sergio
    Serna, Jenifer
    Rueda-Ferreiro, Almudena
    Martin-Suarez, Ana
    Victoria Calvo, M.
    Samuel Perez-Blanco, Jonas
    Lanao, Jose M.
    FRONTIERS IN PHARMACOLOGY, 2022, 13
  • [24] Physiologically-based Pharmacokinetic Modelling for the Reduction of Animal Use in the Discovery of Novel Pharmaceuticals
    Thomas, Simon
    ATLA-ALTERNATIVES TO LABORATORY ANIMALS, 2010, 38 : 81 - 85
  • [25] Predicting Resolvin D1 Pharmacokinetics in Humans with Physiologically-Based Pharmacokinetic Modeling
    Yellepeddi, Venkata K.
    Parashar, Kaustubh
    Dean, Spencer M.
    Watt, Kevin M.
    Constance, Jonathan E.
    Baker, Olga J.
    CTS-CLINICAL AND TRANSLATIONAL SCIENCE, 2021, 14 (02): : 683 - 691
  • [26] A Physiologically-Based Pharmacokinetic Model to Describe Ciprofloxacin Pharmacokinetics Over the Entire Span of Life
    Jan-Frederik Schlender
    Donato Teutonico
    Katrin Coboeken
    Katrin Schnizler
    Thomas Eissing
    Stefan Willmann
    Ulrich Jaehde
    Heino Stass
    Clinical Pharmacokinetics, 2018, 57 : 1613 - 1634
  • [27] Assessment of the Utility of Physiologically-based Pharmacokinetic Model for prediction of Pharmacokinetics in Chinese and Japanese Populations
    Yu, Yanke
    Lin, Jian
    Muto, Chieko
    Li, Yinhua
    Mori, Yuko
    Mittapalli, Rajendar K.
    Tse, Susanna
    Liu, Jian
    Ge, Bei Kang
    Liu, Jing
    INTERNATIONAL JOURNAL OF MEDICAL SCIENCES, 2021, 18 (16): : 3718 - 3727
  • [28] A Physiologically-Based Pharmacokinetic Model to Describe Ciprofloxacin Pharmacokinetics Over the Entire Span of Life
    Schlender, Jan-Frederik
    Teutonico, Donato
    Coboeken, Katrin
    Schnizler, Katrin
    Eissing, Thomas
    Willmann, Stefan
    Jaehde, Ulrich
    Stass, Heino
    CLINICAL PHARMACOKINETICS, 2018, 57 (12) : 1613 - 1634
  • [29] Physiologically-based pharmacokinetic modelling for radiopharmaceuticals using a multilevel object-oriented modelling methodology
    Bouwman, R.
    Herrero, S.
    de With, G.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2021, 48 (SUPPL 1) : S44 - S45
  • [30] A Physiologically-Based Pharmacokinetic Model for the Prediction of Monoclonal Antibody Pharmacokinetics From In Vitro Data
    Jones, Hannah M.
    Zhang, Zhiwei
    Jasper, Paul
    Luo, Haobin
    Avery, Lindsay B.
    King, Lindsay E.
    Neubert, Hendrik
    Barton, Hugh A.
    Betts, Alison M.
    Webster, Robert
    CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 2019, 8 (10): : 738 - 747