Using Population PBPK Modelling to interpret Population PK results exemplified for Levofloxacin in Plasma and Interstitial Fluid
A. Solms (1,2), A. Schäftlein (1,3), M. Zeitlinger (4), C. Kloft (3), W. Huisinga (2)
(1) Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modeling, Martin-Luther-Universitaet Halle-Wittenberg, Freie Universitaet Berlin and Universitaet Potsdam; (2) Computational Physiology Group, Institute of Mathematics, University of Potsdam, Postdam, Germany; (3) Department of Clinical Pharmacy and Biochemistry, Freie Universitaet Berlin, Berlin, Germany; (4) Clinical Pharmacology, Medical University of Vienna, Vienna, Austria;
Objectives: A key aim of covariate modelling is to represent the influence of covariates on PK parameters. Structural parameters, e.g. the volume of distribution (Vss), are lumped parameters with apparent rather than physiological meaning. Therefore it is often difficult to attribute the covariate relation to changes in specific anatomical or physiological characteristics. Based on a novel population (POP) physiological-based PK (PBPK) approach [1] and on densely obtained Levofloxacin (LEV) plasma (PL) & unbound interstitial fluid (ISF) concentrations in adipose (ADI) and muscle (MUS), the objective was to study the impact of variations in anatomy and variations in tissue characteristics on the overall observed variability in Vss.
Material and Methods: We used a generic 13-cmt PBPK model for LEV with tissue-to-unbound-PL partition coefficients according to [2]. The physiological and physiochemical parameters were taken from [2,3,4,5]. In the POP PBPK model the inter-individual variability (IIV) in anatomical parameters, e.g. blood flows and organ weights, was modeled via a LBW-scaling approach [1]. PL and ISF data for LEV and clinical chemistry measurements were collected in [6,7,8]. Fraction unbound, blood-to-PL ratio and clearance values were estimated based on measurements of hematocrit, serum albumin and creatinine- and PL LEV concentrations. A detailed POP PK analysis in NONMEMTM of those data and the related results were provided in [9]. Simulations were performed with R 2.14.1.
Results: POP PK and PBPK predictions could adaequatly describe the measured concentrations and resulted in comparable estimates of the overall Vss. Based on the POP PBPK model the coefficient of variation (CV) on the physiological ADI & MUS volume were predicted to be 33% and 10%. Thereon based predictions of Vss resulted in a likewise 3-fold larger IIV for ADI than MUS (31% vs. 10%). In comparison, the POP PK resulted in an almost identical IIV in Vss ADI & MUS (CV ~74%). Underestimation of IIV in the POP PBPK model might partially be attributable to IIV in tissue composition. This would be consistent with experimental observations [10].
Conclusions: As expected, anatomical IIV can only explain part of the IIV in Vss. Our approach quantified the contribution of the anatomical IIV. Such information is expected to help understanding to what extent a covariate-relationship involving body size descriptors reflects differences in anatomy and to what extent it might reflect an yet unidentified interaction.
References:
[1] A. Solms, S. Pilari, L. Fronton, W. Huisinga, Modelling Inter-Individual Variability in PBPK Models and Deriving Mechanistic Covariate Models for PopPK, PAGE 20, 2011.
[2] T. Rodgers, M. Rowland, Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions, J Pharm Sci 95, 2006.
[3] International Commission on Radiological Protection (ICRP), Basic anatomical and physiological data for use in radiological protection: Reference values, ICRP Publication 89, 2002.
[4] D.N. Fish, A.T. Chow, The Clinical Pharmacokinetics of Levofloxacin, Clin. PK 32 (2), 1997.
[5] T. Uchimura, M. Kato, T. Saito, H. Kinoshita, Prediction of Human Blood-to-Plasma Drug Concentration Ratio, Biopharm Drug Dispos 31, 2010.
[6] R. Bellmann, G. Kuchling, P. Dehghanyar, M. Zeitlinger, E. Minar, B.X. Mayer, M .Müller, C. Joukhadar, Tissue Pharmacokinetics of Levofloxacin in Human Soft Tissue Infections, Br J Clin Pharmacol 57 (5), 2003.
[7] M. Zeitlinger, P. Dehghanyar, B.X. Mayer, B.S. Schenk, U. Neckel, G. Heinz, A Georgopoulos, M. Müller, C. Joukhadar, Relevance of Soft-Tissue Penetration by Levofloxacin for Target Site Bacterial Killing in Patients with Sepsis, Antimicrobial Agents & Chemotherapy 47 (11), 2003.
[8] M. Zeitlinger, F. Traunmüller, A Abrahim, M.R. Müller, Z. Erdogan, M. Müller, C. Joukhadar, A pilot study testing whether concentrations of levofloxacin in interstitial space fluid of soft tissues may serve as a surrogate for predicting its pharmacokinetics in lung, Int J Antimicrobial Agents 29 (1), 2007.
[9] A. Schäftlein, A. Solms, M. Zeitlinger, W. Huisinga, C. Kloft , Microdialysate-corrected mid-interval model versus microdialysate-based integral model - Population pharmacokinetics of levofloxacin in peripheral tissues, PAGE 21, 2012.
[10] D.R. White, E.M. Widdowson, Q. Woodard, J.W.T. Dickerson, The composition of body tissues: (II) Fetus to young adult, British J Radiology 64, 1991.
