David Busse (1,2), Philipp Simon (3), David Petroff (4), Lisa Ehmann (1,2), Christoph Dorn (5), Wilhelm Huisinga (6), Robin Michelet (1), Hermann Wrigge (3), Charlotte Kloft (1)
(1) Dept. of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Germany, (2) and Graduate Research Training program PharMetrX, Germany, (3) Dept. of Anaesthesiology and Intensive Care Medicine and Integrated Research and Treatment Center (IFB), Adiposity Diseases, University of Leipzig, Germany, (4) Clinical Trial Centre Leipzig, University of Leipzig, Germany, (5) Institute of Pharmacy, University of Regensburg, Germany, (6) Institute of Mathematics, University of Potsdam, Germany
Objectives: Meropenem (MER), a broad-spectrum β-lactam antibiotic, is frequently used for the treatment of soft tissue infections, e.g. after surgery. Although obesity has been identified as a risk factor for surgical site infections [1], a quantitative evaluation of its pharmacokinetics (PK) in obese patients is lacking as of date. Nonlinear mixed-effects (NLME) PK analyses of MER in obese patients have been performed previously but none was based on observations providing insights into tissue distribution. The aim of this analysis was to characterise tissue distribution by developing a MER NLME PK model based on concentrations both in plasma and interstitial space fluid (ISF) of subcutaneous (s.c.) adipose tissue.
Methods: The dataset originated from 15 obese (BMI=38.1-81.5 kg/m2) and 15 non-obese patients (BMI=20.5-27.1 kg/m2) treated with 1000 mg MER (30-min i.v.) for infection prophylaxis prior to abdominal surgery. Rich sampling data were available over 8 h in plasma (n=269) and via microdialysis in the ISF of s.c. adipose tissue (n=322). NLME model development was performed in NONMEM® 7.3 using the integrated plasma and micro-/retrodialysis modelling approach [2,3]. Model adequacy was assessed by plausibility and precision of parameter estimates, goodness-of-fit (GOF) plots and visual predictive checks.
Results: Two three-compartment models adequately described MER PK in obese and non-obese patients and yielded precise parameter estimates: (i) a mammillary model with bi-directional distribution between the central and peripheral compartments, and (ii) a catenary model with a chain of two peripheral compartments (ISF attributed to the first peripheral compartment). Parameter estimates of clearance (10.9 and 11.0 L/h, respectively) and total volume of distribution (19.8 L for both models) were similar for both models. Interindividual variability (IIV) was estimated on parameters associated with the central and ISF compartment. For instance, IIV for intercompartmental clearance between the central and ISF-associated compartment (Q1) was 51.0 and 44.6 %CV and for volume of the ISF compartment (V2) 44.9 and 36.6 %CV for the catenary and mammillary model, respectively. Individual parameter estimates of Q1 and V2 were related to body size descriptors (adjusted, lean and total body weight) with correlation coefficients of 0.83 – 0.87 (catenary model) and 0.67 – 0.76 (mammillary model). Based on predictive performance (Akaike information criterion and plots of conditional weighted residuals versus population-predicted concentrations) the catenary model was favoured over the mammillary model.
Conclusions: An NLME PK model was successfully developed to describe concentration-time profiles of MER in obese and non-obese populations. The slightly better predictive performance of a catenary compared to a mammillary PK model could indicate further distribution into more remote tissues such as into intra-abdominal adipose tissues. These tissues have been described as less perfused compared to subcutaneous fat tissue which could explain the delayed distribution via the ISF compartment [4]. To further investigate the remote tissue hypothesis generated via the NLME approach, a physiologically-based PK approach is planned to be applied. Additionally, a covariate analysis will be performed to explore differences in PK parameters between the two patient populations based on both structural models.
References:
[1] R. Winfield, S. Reese, K. Bochicchio et al. Am Surg. 82: 331-336 (2016)
[2] I.K. Minichmayr, A. Schaeftlein, J.L. Kuti et al. Clin. Pharmacokinet. 56: 617-633 (2017).
[3] L. Ehmann, P. Simon P, D. Petroff et al. 27th Population Approach Group Europe (PAGE), Montreux, Switzerland
[4] G. Olga, G. Nina, H. Celia et al. American Heart Association 123: 186-194 (2011)
Reference: PAGE 28 (2019) Abstr 8993 [www.page-meeting.org/?abstract=8993]
Poster: Drug/Disease Modelling - Infection