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/)
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页数:11
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