Dynamically simulating the interaction of midazolam and the CYP3A4 inhibitor itraconazole using individual coupled whole-body physiologically-based pharmacokinetic (WB-PBPK) models

Abstract

Background: Drug-drug interactions resulting from the inhibition of an enzymatic process can have serious implications for clinical drug therapy. Quantification of the drugs internal exposure increase upon administration with an inhibitor requires understanding to avoid the drug reaching toxic thresholds. In this study, we aim to predict the effect of the CYP3A4 inhibitors, itraconazole (ITZ) and its primary metabolite, hydroxyitraconazole (OH-ITZ) on the pharmacokinetics of the anesthetic, midazolam (MDZ) and its metabolites, 1' hydroxymidazolam (1OH-MDZ) and 1' hydroxymidazolam glucuronide (1OH-MDZ-Glu) using mechanistic whole body physiologically-based pharmacokinetic simulation models. The model is build on MDZ, 1OH-MDZ and 1OH-MDZ-Glu plasma concentration time data experimentally determined in 19 CYP3A5 genotyped adult male individuals, who received MDZ intravenously in a basal state. The model is then used to predict MDZ, 1OH-MDZ and 1OH-MDZ-Glu concentrations in an CYP3A-inhibited state following ITZ administration.

Results: For the basal state model, three linked WB-PBPK models (MDZ, 1OH-MDZ, 1OH-MDZ-Glu) for each individual were elimination optimized that resulted in MDZ and metabolite plasma concentration time curves that matched individual observed clinical data. In vivo Km and Vmax optimized values for MDZ hydroxylation were similar to literature based in vitro measures. With the addition of the ITZ/OH-ITZ model to each individual coupled MDZ + metabolite model, the plasma concentration time curves were predicted to greatly increase the exposure of MDZ as well as to both increase exposure and significantly alter the plasma concentration time curves of the MDZ metabolites in comparison to the basal state curves. As compared to the observed clinical data, the inhibited state curves were generally well described although the simulated concentrations tended to exceed the experimental data between approximately 6 to 12 hours following MDZ administration. This deviations appeared to be greater in the CYP3A5 *1/*1 and CYP3A5 *1/*3 group than in the CYP3A5 *3/*3 group and was potentially the result of assuming that ITZ/OH-ITZ inhibits both CYP3A4 and CYP3A5, whereas in vitro inhibition is due to CYP3A4.

Conclusion: This study represents the first attempt to dynamically simulate metabolic enzymatic drug-drug interactions via coupled WB-PBPK models. The workflow described herein, basal state optimization followed by inhibition prediction, is novel and will provide a basis for the development of other inhibitor models that can be used to guide, interpret, and potentially replace clinical drug-drug interaction trials.

Figure 1

a) Schematic representation of the CYP3A4-, CYP3A5- and UGT-catalyzed metabolic pathway for MDZ. The percentages of the administered MDZ dose which are excreted in the urine within 24 hours [11,12] are given below the compounds. Abbreviations: MDZ, midazolam; 1OH-MDZ, 1-hydroxymidazolam; 4OH-MDZ, 4-hydroxymidazolam; 1,4-di-OH-MDZ, 1,4-dihydroxymidazolam; 1OH-MDZ-Glu, 1-hydroxymidazolam glucuronide; 1,4-di-OH-MDZ-Glu, 1,4-dihydroxymidazolam glucuronide; 4-OH-MDZ-Glu, 4-hydroxymidazolam glucuronide; UGT, uridine diphosphate-glucuronosyl-transferase. b) Schematic representation of the simplified metabolic pathway for MDZ used in the coupled model approach.

Figure 2

Simulated plasma concentration time curve (line) of a) MDZ following a 15 minute intravenous infusion of 0.1 mg/kg MDZ and b) 1OH-MDZ following a 15 minute intravenous infusion of 0.15 mg/kg 1OH-MDZ as compared to mean experimental data. Experimental data was taken from Mandema et al. [15].

Figure 3

Mean of the predicted plasma concentration time curves (line) of MDZ and 1OH-MDZ following an intravenous administration of 0.15 mg/kg as compared to the mean (n = 6) of the experimental data. Experimental data was taken from Heizmann et al. [11].

Figure 4

Predicted plasma concentration time curves (lines) of MDZ, 1OH-MDZ and 1OH-MDZ-Glu following an intravenous bolus administration of 1 mg MDZ as compared to experimental data from a typical individual (symbols). Experimental data was taken from Kharasch et al. [16].

Figure 5

Mean ± standard deviation plots of the experimental observed plasma concentration time profiles for MDZ and the sum of its metabolites, 1OH-MDZ and 1OH-MDZ-Glu (symbols: squares) and the corresponding mean elimination optimized curves (solid lines) for the CYP3A5 genotypes, CYP3A5 *1/*1 (n = 6), CYP3A5 *1/*3 (n = 6) and CYP3A5*3/*3 (n = 7) in the basal state. Also presented is the mean ± standard deviation plots of the experimental observed plasma concentration time profiles for MDZ and the sum of its metabolites, 1OH-MDZ and 1OH-MDZ-Glu (symbols: circles) and the corresponding mean predicted curves (dotted lines) for the CYP3A5 genotypes in the CYP3A inhibited state resulting from ITZ administration. Plasma concentration time curves in the inhibited state were graphed starting at time = 0 to allow for direct comparison with the basal state curves. Experimental data was taken from Yu et al. [4].

Figure 6

Rank-order plots for the optimized 1OH_K_m and 1OH_V_max values normalized to the mean.

Figure 7

Simulated (line) ITZ plasma concentration time curve following 15 days of 200 mg once per day ITZ administration in the fed state. Mean experimental data (symbols: squares) for the first day was taken from Barone et al. [17] and experimental data (mean ± SD, symbols: circles) was taken from Hardin et al. [18]. The inset graph presents the estimated fraction of remaining CYP3A activity over time as a result of ITZ and OH-ITZ inhibition.

Figure 8

Comparison of observed and predicted tissue/plasma partition coefficients for the various organs (left: log-log plot, right: enlarged linear plot of the interval [0,3.5]). Small symbols indicate individual data reported by Björkmann et al. [22], large symbols denote the mean value for each organ. The solid line represents the identity, the dotted lines mark the region 3-fold-off the identity.

Figure 9

Fraction of remaining CYP3A activity in pooled human liver microsomes in relation to the unbound inhibitor concentrations of ITZ and OH-ITZ. Data points, as taken from Isoherranen et al. [13], were fit to a logistic, 3 parameter model and the equations were used to generate a percentage of CYP3A activity remaining, constrained between 0 and 1.

Figure 10

Schematic representation of the inhibition of the CYP3A4-catalyzed MDZ hydroxylation by ITZ and its major metabolite OH-ITZ (a) and the simplified model used in this study (b).