Understanding Voriconazole Metabolism: A Middle-Out Physiologically-Based Pharmacokinetic Modelling Framework Integrating In Vitro and Clinical Insights

Abstract

Background and objective: Voriconazole (VRC), a broad-spectrum antifungal drug, exhibits nonlinear pharmacokinetics (PK) due to saturable metabolic processes, autoinhibition and metabolite-mediated inhibition on their own formation. VRC PK is also characterised by high inter- and intraindividual variability, primarily associated with cytochrome P450 (CYP) 2C19 genetic polymorphism. Additionally, recent in vitro findings indicate that VRC main metabolites, voriconazole N-oxide (NO) and hydroxyvoriconazole (OHVRC), inhibit CYP enzymes responsible for VRC metabolism, adding to its PK variability. This variability poses a significant risk of therapeutic failure or adverse events, which are major challenges in VRC therapy. Understanding the underlying processes and sources of these variabilities is essential for safe and effective therapy. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) modelling framework that elucidates the complex metabolism of VRC and the impact of its metabolites, NO and OHVRC, on the PK of the parent, leveraging both in vitro and in vivo clinical data in a middle-out approach.

Methods: A coupled parent-metabolite PBPK model for VRC, NO and OHVRC was developed in a stepwise manner using PK-Sim^® and MoBi^®. Based on available in vitro data, NO formation was assumed to be mediated by CYP2C19, CYP3A4, and CYP2C9, while OHVRC formation was attributed solely to CYP3A4. Both metabolites were assumed to be excreted via renal clearance, with hepatic elimination also considered for NO. Inhibition functions were implemented to describe the complex interaction network of VRC autoinhibition and metabolite-mediated inhibition on each CYP enzyme.

Results: Using a combined bottom-up and middle-out approach, incorporating data from multiple clinical studies and existing literature, the model accurately predicted plasma concentration-time profiles across various intravenous dosing regimens in healthy adults, of different CYP2C19 genotype-predicted phenotypes. All (100%) of the predicted area under the concentration-time curve (AUC) and 94% of maximum concentration (C_max) values of VRC met the 1.25-fold acceptance criterion, with overall absolute average fold errors of 1.12 and 1.14, respectively. Furthermore, all predicted AUC and C_max values of NO and OHVRC met the twofold acceptance criterion.

Conclusion: This comprehensive parent-metabolite PBPK model of VRC quantitatively elucidated the complex metabolism of the drug and emphasised the substantial impact of the primary metabolites on VRC PK. The comprehensive approach combining bottom-up and middle-out modelling, thereby accounting for VRC autoinhibition, metabolite-mediated inhibition, and the impact of CYP2C19 genetic polymorphisms, enhances our understanding of VRC PK. Moreover, the model can be pivotal in designing further in vitro experiments, ultimately allowing for extrapolation to paediatric populations, enhance treatment individualisation and improve clinical outcomes.

Fig. 1

Overview of the PBPK model development workflow for voriconazole and its metabolites, illustrating the input/data sources (inside boxes, left side) and the achieved steps (inside boxes, right side). ADME absorption, distribution, metabolism, excretion, CYP cytochrome P450, gXM genotype-predicted phenotype, H hypothesis, IM intermediate metaboliser, i.v. intravenous, MM Michaelis–Menten, NM normal metaboliser, NO voriconazole N-oxide, OHVRC hydroxyvoriconazole, PBPK physiologically-based pharmacokinetic, PM poor metaboliser, RM rapid metaboliser, VRC voriconazole

Fig. 2

Whole-body intravenous physiologically-based pharmacokinetic model for voriconazole and its metabolites (left) and schematic representation of voriconazole elimination pathways (right). Voriconazole is metabolised by CYP2C19, CYP3A4 and CYP2C9 into voriconazole N-oxide, with subsequent elimination via a nonspecific hepatic route and excreted via renal clearance. The remainder of voriconazole absorbed is transformed via CYP3A4 into hydroxyvoriconazole, which is further metabolised and excreted via renal clearance. The parent compound and both metabolites concomitantly inhibit CYP2C19, CYP3A4 and CYP2C9. CYP cytochrome P450

Fig. 3

Predicted total plasma concentration-time profiles of VRC (orange) and NO (blue) comparing a priori (left) versus a posteriori models of hypothesis 1 (middle) and hypothesis 2 (right) following administration of a single intravenous dose of 50 mg infused over 2 h. Geometric means of the observed data are shown as dots, with error bars indicating geometric standard deviation for CYP2C19 IM (top), CYP2C19 NM (middle), and CYP2C19 RM (bottom) individuals in the study by Hohmann et al. (N = 15) [13]. Solid lines represent the geometric mean of the respective population predictions (N = 1000) and the shaded area represents the 90% population prediction intervals. CYP cytochrome P450, gXM genotype-predicted phenotype, H hypothesis, IM intermediate metaboliser, N number of individuals, NM normal metaboliser, NO voriconazole N-oxide, RM rapid metaboliser, VRC voriconazole

Fig. 4

Predicted total plasma concentration-time profiles of VRC (orange) and NO (blue) comparing a priori (left) versus a posteriori models of hypothesis 1 (middle) and hypothesis 2 (right) following administration of a single intravenous dose of 100 mg infused over 4 h. Geometric means of the observed data are shown as dots, with error bars indicating geometric standard deviation for CYP2C19 IM (top), CYP2C19 NM (middle), and CYP2C19 RM (bottom) individuals in the study by Hohmann et al. (N = 12) [30]. Solid lines represent the geometric mean of the respective population predictions (N = 1000) and the shaded area represents the 90% population prediction intervals. CYP cytochrome P450, gXM genotype-predicted phenotype, H hypothesis, IM intermediate metaboliser, N number of individuals, NM normal metaboliser, NO voriconazole N-oxide, RM rapid metaboliser, VRC voriconazole

Fig. 5

Predicted total plasma concentration-time profiles following a single intravenous dose of 400 mg voriconazole infused over 2 h. a Concentration-time profiles for VRC (orange) comparing a priori versus a posteriori models of hypothesis 1 and hypothesis 2. b Concentration-time profiles for NO (blue) and OHVRC (red) comparing a priori versus a posteriori models of hypothesis 1 and hypothesis 2. Geometric means of the observed data are shown as dots, with error bars indicating the geometric standard deviation for CYP2C19 PM (first row), CYP2C19 IM (second row), CYP2C19 NM (third row), and CYP2C19 RM (bottom row) individuals in the studies by Scholz et al. [14], Hohmann et al. [13] and Hohmann et al. [30] (N = 47). Solid lines represent the geometric mean of the respective population predictions (N = 1000) and the shaded area represents the 90% population prediction intervals. CYP cytochrome P450, gXM genotype-predicted phenotype, H hypothesis, IM intermediate metaboliser, N number of individuals, NM normal metaboliser, NO voriconazole N-oxide, OHVRC hydroxyvoriconazole, PM poor metaboliser, RM rapid metaboliser, VRC voriconazole

Fig. 6

Predicted total plasma concentration-time profiles of VRC (orange) comparing a priori (left) versus a posteriori models of hypothesis 1 (middle) and hypothesis 2 (right) after multiple intravenous administrations of 3 mg/kg qd on the first day, then a MD of 3 mg/kg bid starting day 3 until day 11 of Study A by Purkins et al. [37]. a Arithmetic mean (N = 9) of the observed concentration-time profiles for days 1, 5, 8 and 12. b Individual observed plasma C_max values for VRC on days 1, 5, 8 and 12. Solid orange lines represent the geometric mean of population predictions (N = 1000); dots represent the observed data for arithmetic means in panel (a) and individual C_max values in panel (b); shaded area represents the 90% population prediction intervals; solid blue and red lines represent the geometric mean of population predictions (N = 1000) for NO and OHVRC, respectively; and the dashed black line represents the lower limit of quantification for VRC. bid twice daily, C_max maximum plasma concentration, IV intravenous, MD maintenance dose, N number of individuals, NO voriconazole N-oxide, OHVRC hydroxyvoriconazole, qd once daily, VRC voriconazole

Fig. 7

Predicted total plasma concentration-time profiles of VRC (orange) comparing a priori (left) versus a posteriori models of hypothesis 1 (middle) and hypothesis 2 (right), after multiple IV administration of an LD of 6 mg/kg bid on the first day, then an MD of 3 mg/kg bid starting on day 2 until day 9 of the Study B by Purkins et al. [37]. a Arithmetic mean (N = 9) of the observed concentration-time profiles for days 1, 3, 6 and 10. b Individual observed plasma C_min values for VRC for the 1st to 10th days of the MD. Solid orange lines represent the geometric mean of population predictions (N = 1000); the dots represent the observed data for arithmetic means in panel (a) and individual C_min values in panel (b); shaded area represents the 90% population prediction intervals; solid blue and red lines represent the geometric mean of the population predictions (N = 1000) for NO and OHVRC, respectively; and the dashed black line represents the lower limit of quantification for VRC. bid twice daily, C_min minimum plasma concentration, H hypothesis, IV intravenous, LD loading dose, MD maintenance dose, N number of individuals, NO voriconazole N-oxide, OHVRC hydroxyvoriconazole, VRC voriconazole

Fig. 8

Goodness-of-fit plots for plasma concentration predictions. A comparison of a priori (left) versus final a posteriori prediction for hypothesis 1 (middle) and hypothesis 2 (right) across CYP2C19 gXM. (a) Individual observed versus predicted VRC total plasma concentrations from all studies (IVSD and IVMD, n = 1765). (b) Individual observed versus predicted NO total plasma concentrations from all IVSD studies (n = 1591). (c) Individual observed versus predicted OHVRC total plasma concentrations from all IVSD studies (n = 372). The solid line represents the identity line, with dotted and dashed lines indicating 1.25-fold and 2-fold deviations, respectively. CYP cytochrome P450, gXM genotype-predicted phenotype, H hypothesis, IM intermediate metaboliser, IVSD intravenous single dose, IVMD intravenous maintenance dose, n number of samples, NO voriconazole N-oxide, NM normal metaboliser, OHVRC hydroxyvoriconazole, PM poor metaboliser, RM rapid metaboliser, VRC voriconazole

Fig. 9

Pharmacokinetic parameter goodness-of-fit plots comparing a priori (left) versus a posteriori of hypothesis 1 (middle) and hypothesis 2 (right) models. a Predicted AUC_last and b C_max of VRC, NO and OHVRC compared with observed values for all single IV dose studies. The solid line represents the line of identity; dotted lines indicate 1.25-fold, and dashed lines indicate 2-fold deviation. AUC_last area under the concentration-time curve from the time of administration to the last measurable concentration, C_max maximum plasma concentration, IM intermediate metaboliser, IV intravenous, NO voriconazole N-oxide, NM normal metaboliser, OHVRC hydroxyvoriconazole, PM poor metaboliser, RM rapid metaboliser, VRC voriconazole