Xia Li1, Sebastian Frechen2, Daniel Moj3, Max Taubert1, Chih-hsuan Hsin1, Gerd Mikus4, Thorsten Lehr3, Uwe Fuhr1
1 University of Cologne, Faculty of Medicine and University Hospital Cologne, Center for Pharmacology, Department I of Pharmacology; Cologne, Germany; 2 Clinical Pharmacometrics, Bayer AG, Leverkusen, Germany; 3 Department of Pharmacy, Clinical Pharmacy, Saarland University; Saarbrücken, Germany; 4 Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg; Heidelberg, Germany.
Objectives: Voriconazole, a first-line anti-fungal therapy, exhibits nonlinear pharmacokinetics together with large inter-individual variability but has a narrow therapeutic range. We aim to investigate the metabolism of voriconazole to better understand dose- and time-dependent alterations in the pharmacokinetics of the drug and to provide the model basis for safe and effective use according to CYP2C19 genotype.
Methods: In vitro assays were conducted to assess mechanism-based inactivation (MBI) of CYP3A4 by voriconazole. These results were combined with 93 published concentration-time curves of voriconazole from clinical trials to develop a whole-body physiologically-based pharmacokinetic (PBPK) model for healthy volunteers. The model was evaluated with the predicted/observed ratio of AUC and Cmax, geometric mean fold error, as well as the comparison of predicted with observed concentration-time curves from virtual studies over the full range of voriconazole administration dosage regimen (including intravenous and oral, dosing from 1.5 to 6 mg/kg and from 50 to 400 mg). Subsequently, the voriconazole model was coupled with independently developed CYP3A4 substrate models (midazolam and alfentanil) to assess the validity of the model to describe the inhibitory effects of voriconazole on CYP3A4. Sensitivity analysis was conducted for parameters: i) optimized; ii) related to optimized parameters; iii) a strong influence on calculation methods used in the model; iv) significant impact on the model.
Results: The IC50 shift assay showed that voriconazole has a MBI on CYP3A4 with a 16-fold difference in the absence and presence of NADPH. The inactivation kinetic assay provided a KI of 9.33 (95% confidence interval: 2.56 to 34.0) μM, supporting the integration of MBI model into the PBPK model. Genetic polymorphisms of CYP2C19 were introduced into the model for rapid metabolizers (RMs, CYP2C19*1/*17 or *1/*17), extensive metabolizers (EMs, *1/*1), intermediate metabolizers (IMs, *1/*2,*1/*3,*2/*17, *2/*2/*17) or poor metabolizers (PMs, *2/*2, *3/*3 or *2/*3) with the CYP2C19 expression values of 0.79, 0.76, 0.40, and 0.01 µmol/L, respectively[1]. PBPK model verification demonstrated good performance of the model, with 82% of predicted/observed AUC ratios and all Cmax ratios from 28 test datasets being within a 2-fold range. For those studies reporting CYP2C19 genotype, 88% of AUC ratios and 95% of Cmax ratios were inside the 2-fold range of 41 test profiles. Sensitivity analysis showed that the PBPK model of voriconazole was most sensitive to CYP2C19 kcat, CYP2C19 Km and fraction unbound values (all taken from the literature), with a sensitivity value exceeding a range of -0.5 to 0.5. For the effect of voriconazole on midazolam and alfentanil, the predicted/observed AUC change for these CYP3A4 substrates by voriconazole ranged from 1.01 to 1.36, indicating that CYP3A4 inhibition was appropriately incorporated into the voriconazole model.
Conclusions: Both the in vitro assay and model-based simulations confirmed the MBI of CYP3A4 by voriconazole as a pivotal characteristic of the drug’s pharmacokinetics. The PBPK model developed here could support individual dose adjustment of voriconazole, also according to genetic polymorphisms of CYP2C19, and DDI risk management.
References:
[1]Shirasaka Y, Chaudhry AS, McDonald M, Prasad B, Wong T, Calamia JC, et al. Interindividual variability of CYP2C19-catalyzed drug metabolism due to differences in gene diplotypes and cytochrome P450 oxidoreductase content. Pharmacogenomics J. Nature Publishing Group; 2016;16:375–87.
Reference: PAGE 28 (2019) Abstr 8995 [www.page-meeting.org/?abstract=8995]
Poster: Drug/Disease Modelling - Infection