Franziska Kluwe (1,2), Claudia Kirbs (1,3), Franziska Drescher (3), Peter Matzneller (4), Wilhelm Huisinga (5), Markus Zeitlinger (4), Charlotte Kloft (1)
(1) Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Germany, (2) Graduate Research Training Program PharMetrX, Germany, (3) Department of Clinical Pharmacy, Institute of Pharmacy, Martin-Luther-Universitaet Halle-Wittenberg, Germany, (4) Department of Clinical Pharmacology, Medical University of Vienna, Austria, (5) Institute of Mathematics, University of Potsdam, Germany
Objectives: Voriconazole (VRC) is an antifungal drug used for prophylaxis and therapy of various fungal infections. The main route of elimination from the systemic circulation is CYP-mediated hepatic metabolism to various (inactive) metabolites [1]. High variability and nonlinearity in VRC pharmacokinetics were described previously [2]. Nonlinearity in the elimination of VRC is mainly attributed to suspected saturation and autoinhibition of metabolising CYP enzymes. Understanding the pharmacokinetics of VRC and identify factors contributing to the high variability is crucial for ensuring safe and effective concentrations. Therefore, the current work aimed at investigating the pharmacokinetics of VRC in healthy volunteers after approved sequence dosing with a special focus on a (semi-)mechanistic implementation of nonlinear elimination. A covariate analysis was performed to identify factors (e.g. CYP2C19 genotype) or other aspects (time- and/or concentration- and/or formulation-dependency) explaining the high pharmacokinetic variability.
Methods: An exhaustive literature research using PubMed [3] was performed to identify structural models used to describe the pharmacokinetics of VRC and covariates potentially explaining the pharmacokinetic variability. The clinical data used for model development arose from a prospective, open-labelled, uncontrolled study conducted in collaboration with the Medical University of Vienna (Eudra-CT: 2008-008524-32) [2]. Ten healthy male individuals (age: 21–46 years, weight: 65–83 kg) received the approved sequence dosing regimen for VRC of initially short-term i.v. infusions and subsequently p.o. administrations every 12 hours (2×6 mg/kg i.v., 2×4 mg/kg i.v., 3×200 mg p.o.). Intensive plasma sampling was carried out over 4 days and VRC plasma concentrations were determined after ultrafiltration by high-performance liquid chromatography [4]. Among several other continuous and categorical other covariates, CYP2C9 and CYP2C19 genotyping data was available for the study participants. All data was analysed using R, RStudio and NONMEM together with PsN, Pirana and Xpose4 [5-8]. To assess the model performance, precision of parameter estimates and graphical model evaluation techniques, such as goodness-of-fit plots and visual-predictive checks, were utilised.
Results: Different structural models describing the pharmacokinetics of VRC and factors (covariates) potentially explaining the pharmacokinetic variability were identified. Among the published models for VRC, clearance was implemented aslinear clearance, (time-dependent) nonlinear (Michaelis-Menten) clearance or parallel linear and (time-dependent) nonlinear (Michaelis-Menten) clearance [9]. In addition, in this work, to more mechanistically describe the pharmacokinetics and study the time course of VRC autoinhibition, usage of an additional inhibition compartment [10] and an enzyme turn-over model was investigated [11]. VRC plasma concentrations were best described by a two-compartment distribution model (191 and 469 L) with an absorption rate constant of 0.91 h-1, bioavailability of 85% and intercompartmental clearance of 79.6 L/h. In the final model, clearance (10.7 L/h) was inhibited over time to approximately a third of its original value, dependent on the concentrations in an additional inhibition compartment. Interindividual variability for clearance, volumes of distribution, absorption rate constant and bioavailability using an exponential model was highest for clearance (65.4 CV%). Implementation of CYP2C19 genotyping information (presence or absence of CYP2C19*2), explained around half of the interindividual variability in clearance.
Conclusions: The developed model adequately characterised the pharmacokinetics of VRC in plasma using a semi-mechanistic approach integrating knowledge about the metabolism of VRC. Substantial interindividual variabilitywas identified especially in clearance of healthy volunteers despite standard dosing, mainly due to differences in CYP2C19 genotype. As a next step, using prior information from in vitro or bottom-up (PBPK) models and metabolite data (plasma/urine) to inform the model and different pathways, can further contribute to elucidate and understand VRC pharmacokinetics, to explain the high variability by covariates and to draw consequences for optimal dosing.
References:
[1] J.M. Barbarino, A. Owusu Obeng, T.E. Klein, R.B. Altman. Pharmacogenet. Genomics 22: 201–209(2017).
[2] C. Kirbs, F. Kluwe, F. Drescher, E. Lackner, P. Matzneller, J. Weiss, M. Zeitlinger, C. Kloft. Eur. J. Pharm. Sci. 131: 218–229 (2019).
[3] https://www.ncbi.nlm.nih.gov/pubmed/
[4] F. Simmel, J. Soukup, A. Zoerner, J. Radke, C. Kloft. Anal. Bioanal. Chem. 392: 479–488 (2008).
[5] R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2018).
[6] RStudio Team RStudio: Integrated Development for R. RStudio, Inc., Boston, MA. URL http://www.rstudio.com/ (2016).
[7] S.L. Beal, L.B. Sheiner, A.J. Boeckmann. NONMEM 7.3.0 User’s Guides. Icon Development Solutions, Hanover, MD, USA (1989-2013).
[8] R.J. Keizer, M.O. Karlsson, A. Hooker. CPT Pharmacometrics Syst. Pharmacol. 2: e50 (2013).
[9] D.A.J. McDougall, J. Martin, E.G. Playford, B. Green. J. Pharmacokinet. Pharmacodyn. 43: 1–13 (2015).
[10] N. Plock, C. Buerger, C. Joukhadar, S. Kljucar, C. Kloft. Drug Metab. Dispos. 35: 1816–1823 (2007).
[11] W. Smythe, A. Khandelwal, C. Merle, R. Rustomjee, M. Gninafon, M. Bocar Lo, O.B. Sow, P.L. Olliaro, C. Lienhardt, J. Horton, P. Smith, H. McIlleron, U.S.H. Simonsson. Antimicrob. Agents Chemother. 56: 2091–2098 (2012).
Reference: PAGE 28 (2019) Abstr 9130 [www.page-meeting.org/?abstract=9130]
Poster: Drug/Disease Modelling - Infection