Effect of High-Dose Esomeprazole on CYP1A2, CYP2C19, and CYP3A4 Activities in Humans: Evidence for Substantial and Long-lasting Inhibition of CYP2C19.

In vitro, esomeprazole is a time-dependent inhibitor of CYP2C19. Additionally, racemic omeprazole induces CYP1A2 and omeprazole and its metabolites inhibit CYP3A4 in vitro. In this 5-phase study, 10 healthy volunteers ingested 20 mg pantoprazole, 0.5 mg midazolam, and 50 mg caffeine as respective index substrates for CYP2C19, 3A4, and 1A2 before and 1, 25, 49 (pantoprazole only), and 73 hours after an 8-day pretreatment with 80 mg esomeprazole twice daily. The area under the plasma concentration-time curve (AUC) of R-pantoprazole increased 4.92-fold (90% confidence interval (CI) 3.55-6.82), 2.31-fold (90% CI 1.85-2.88), and 1.33-fold (90% CI 1.06-1.68) at the 1-hour, 25-hour, and 73-hour phases, respectively, consistent with a substantial and persistent inhibition of CYP2C19. The AUC of midazolam increased up to 1.44-fold (90% CI 1.22-1.72) and the paraxanthine/caffeine metabolic ratio up to 1.19-fold (90% CI 1.04-1.36), when the index substrates were taken 1 hour after esomeprazole. Based on the recovery of R-pantoprazole oral clearance, the turnover half-life of CYP2C19 was estimated to average 53 hours. Pharmacokinetic simulation based on the observed concentrations of esomeprazole and its metabolites as well as their published CYP2C19 inhibitory constants was well in line with the observed changes in R-pantoprazole pharmacokinetics during the course of the study. Extrapolations assuming linear pharmacokinetics of esomeprazole suggested weak to moderate inhibition at 20 and 40 mg twice daily dosing. In conclusion, high-dose esomeprazole can cause strong inhibition of CYP2C19, but only weakly inhibits CYP3A4 and leads to minor induction of CYP1A2. The enzymatic activity of CYP2C19 recovers gradually in ~ 3-4 days after discontinuation of esomeprazole treatment.

Assessing CYP2C19 Ontogeny in Neonates and Infants Using Physiologically Based Pharmacokinetic Models: Impact of Enzyme Maturation Versus Inhibition.

The objective of this study was to develop pediatric physiologically based pharmacokinetic (PBPK) models for pantoprazole and esomeprazole. Pediatric PBPK models were developed by Simcyp version 15 by incorporating cytochrome P450 (CYP)2C19 maturation and auto-inhibition. The predicted-to-observed pantoprazole clearance (CL) ratio ranged from 0.96-1.35 in children 1-17 years of age and 0.43-0.70 in term infants. The predicted-to-observed esomeprazole CL ratio ranged from 1.08-1.50 for children 6-17 years of age, and 0.15-0.33 for infants. The prediction was markedly improved by assuming no auto-inhibition of esomeprazole in infants in the PBPK model. Our results suggested that the CYP2C19 auto-inhibition model was appropriate for esomeprazole in adults and older children but could not be directly extended to infants. A better understanding of the complex interplay of enzyme maturation, inhibition, and compensatory mechanisms for CYP2C19 is necessary for PBPK modeling in infants.

Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models.

Polypharmacy increases the risk of drug-drug interactions (DDIs). Combining epidemiological studies with pharmacokinetic modeling, we detected and evaluated high-dimensional DDIs among 30 frequent drugs. Multidrug combinations that increased the risk of myopathy were identified in the US Food and Drug Administration Adverse Event Reporting System (FAERS) and electronic medical record (EMR) databases by a mixture drug-count response model. CYP450 inhibition was estimated among the 30 drugs in the presence of 1 to 4 inhibitors using in vitro / in vivo extrapolation. Twenty-eight three-way and 43 four-way DDIs had significant myopathy risk in both databases and predicted increases in the area under the concentration-time curve ratio (AUCR) >2-fold. The high-dimensional DDI of omeprazole, fluconazole, and clonidine was associated with a 6.41-fold (FAERS) and 18.46-fold (EMR) increased risk of myopathy local false discovery rate (<0.005); the AUCR of omeprazole in this combination was 9.35. The combination of health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high-dimensional DDIs.

The Effects of Fluvoxamine on the Steady-State Plasma Concentrations of Escitalopram and Desmethylescitalopram in Depressed Japanese Patients.

BACKGROUND: The aim of this study was to determine the impact of fluvoxamine, an inhibitor of Cytochrome P450 (CYP) 2C19 (CYP2C19), on the pharmacokinetics of escitalopram, a substrate of CYP2C19. METHODS: Thirteen depressed patients initially received a 20-mg/d dose of escitalopram alone. Subsequently, a 50-mg/d dose of fluvoxamine was administered because of the insufficient efficacy of escitalopram. Plasma concentrations of escitalopram and desmethylescitalopram were quantified using high-performance liquid chromatography before and after fluvoxamine coadministration. The QT and corrected QT (QTc) intervals were measured before and after fluvoxamine coadministration. RESULTS: Fluvoxamine significantly increased the plasma concentrations of escitalopram (72.3 ± 36.9 ng/mL versus 135.2 ± 79.7 ng/mL, P < 0.01) but not those of desmethylescitalopram (21.5 ± 7.0 ng/mL versus 24.9 ± 12.0 ng/mL, no significance [ns]). The ratios of desmethylescitalopram to escitalopram were significantly decreased during fluvoxamine coadministration (0.37 ± 0.21 versus 0.21 ± 0.10, P < 0.01). The CYP2C19 genotype did not fully explain the degree of the change. Fluvoxamine coadministration did not change the QT or QTc intervals. CONCLUSIONS: The results of this study suggest that adjunctive treatment with fluvoxamine increases the concentration of escitalopram. The QTc interval did not change in this condition.

Use of three-compartment physiologically based pharmacokinetic modeling to predict hepatic blood levels of fluvoxamine relevant for drug-drug interactions.

Using a three-compartment physiologically based pharmacokinetic (PBPK) model and a tube model for hepatic extraction kinetics, equations for calculating blood drug levels (Cb s) and hepatic blood drug levels (Chb s, proportional to actual hepatic drug levels), were derived mathematically. Assuming the actual values for total body clearance (CLtot ), oral bioavailability (F), and steady-state distribution volume (Vdss ), Cb s, and Chb s after intravenous and oral administration of fluvoxamine (strong perpetrator in drug-drug interactions, DDIs), propranolol, imipramine, and tacrine were simulated. Values for Cb s corresponded to the actual values for all tested drugs, and mean Chb and maximal Chb -to-maximal Cb ratio predicted for oral fluvoxamine administration (50 mg twice-a-day administration) were nearly 100 nM and 2.3, respectively, which would be useful for the predictions of the DDIs caused by fluvoxamine. Fluvoxamine and tacrine are known to exhibit relatively large F values despite having CLtot similar to or larger than hepatic blood flow, which may be because of the high liver uptake (almost 0.6) upon intravenous administration. The present method is thus considered to be more predictive of the Chb for perpetrators of DDIs than other methods.