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

• Published in: International journal of antimicrobial agents Vol. 64; no. 6; p. 107352
• Main Authors: Bahnasawy, Salma M., Parrott, Neil J., Gijsen, Matthias, Spriet, Isabel, Friberg, Lena E., Nielsen, Elisabet I.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Meropenem; PBPK; Sepsis; PBPK; Sepsis; Meropenem
• ISSN: 0924-8579; 1872-7913; 1872-7913
• DOI: 10.1016/j.ijantimicag.2024.107352

•A physiologically-based pharmacokinetic model adapted to the pathophysiology in sepsis was able to describe the plasma and subcutaneous tissue exposure of meropenem in septic patients.•Changes in meropenem distribution may be linked to expected changes in body composition (especially interstitial space volume) in sepsis patients.•The model could be further expanded to provide a generic physiological modelling framework in septic patients by applying it to other entities with different physicochemical and PK properties. 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. 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. The model-predicted PK metrics (AUC, Cmax, CL, Vss) 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, Cmax, and CL had a fold-error ratio range of 0.98–1.12, with alignment of the predicted and observed variability. For Vss, 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 Vss was sensitive to the unbound fraction, organ volume fraction of the interstitial compartment, and the organ volume. These findings may be extended to more diverse drug types and support a more mechanistic understanding of the effect of sepsis on drug exposure. [Display omitted]