Effect of fluoxetine on the pharmacokinetics of lansoprazole: a two-treatment period study in healthy male subjects

Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole

This study aimed to determine the influence of proton pump inhibitors (PPIs) on the pharmacokinetics of voriconazole and to characterise potential drug-drug interactions (DDIs) between voriconazole and various PPIs (omeprazole, esomeprazole, lansoprazole and rabeprazole). Using adjusted physicochemical data and the pharmacokinetic (PK) parameters of voriconazole and PPIs, physiologically based pharmacokinetic (PBPK) models were built and were verified in healthy subjects using GastroPlus^TM to predict the plasma concentration-time profiles of voriconazole and PPIs. These models were then used to assess potential DDIs for voriconazole when administered with PPIs. The results indicated the PBPK model-simulated plasma concentration-time profiles of both voriconazole and PPIs were consistent with the observed profiles. In addition, the DDI simulations suggested that the PK values of voriconazole increased to various degrees when combined with several PPIs. The area under the plasma concentration-time curve for the time of the simulation (AUC_0-t) of voriconazole was increased by 39%, 18%, 12% and 1% when co-administered with omeprazole, esomeprazole, lansoprazole and rabeprazole, respectively. Omeprazole was the most potent CYP2C19 inhibitor tested, whereas rabeprazole had no influence on voriconazole (omeprazole > esomeprazole > lansoprazole > rabeprazole). However, in consideration of the therapeutic concentration range, dosage adjustment of voriconazole is unnecessary regardless of which PPI was co-administered.

Application of nanoparticles for oral delivery of acid-labile lansoprazole in the treatment of gastric ulcer: in vitro and in vivo evaluations

The aim of this study was to develop nanoparticles for oral delivery of an acid-labile drug, lansoprazole (LPZ), for gastric ulcer therapy. LPZ-loaded positively charged Eudragit(®) RS100 nanoparticles (ERSNPs-LPZ) and negatively charged poly(lactic-co-glycolic acid) nanoparticles (PLGANPs-LPZ) were prepared. The effect of charge on nanoparticle deposition in ulcerated and non-ulcerated regions of the stomach was investigated. The cellular uptake of nanoparticles in the intestine was evaluated in a Caco-2 cell model. The pharmacokinetic performance and ulcer healing response of LPZ-loaded nanoparticles following oral administration were evaluated in Wistar rats with induced ulcers. The prepared drug-loaded ERSNPs-LPZ and PLGANPs-LPZ possessed opposite surface charge (+38.5±0.3 mV versus -27.3±0.3 mV, respectively) and the particle size was around 200 nm with a narrow size distribution. The negatively charged PLGANPs adhered more readily to the ulcerated region (7.22%±1.21% per cm(2)), whereas the positively charged ERSNPs preferentially distributed in the non-ulcerated region (8.29%±0.35% per cm(2)). Both ERSNPs and PLGANPs were prominent uptake in Caco-2 cells, too. The nanoparticles sustained and prolonged LPZ concentrations up to 24 hours, and the half-life and mean residence time of LPZ were prolonged by 3.5-fold and 4.5-fold, respectively, as compared with LPZ solution. Oral administration of LPZ-loaded nanoparticles healed 92.6%-95.7% of gastric ulcers in Wistar rats within 7 days.

Prediction of inter-individual variability in the pharmacokinetics of CYP2C19 substrates in humans

Significant inter-individual variability of exposure for CYP2C19 substrates may be only partly due to genetic polymorphism. Therefore, the in vivo inter-individual variability in hepatic intrinsic clearance (CL(int,h)) of CYP2C19 substrates was estimated from reported AUC values using Monte Carlo simulations. The coefficient of variation (CV) for CL(int,h) in poor metabolizers (PM) expected from genotypes CYP2C19*2/*2, CYP2C19*3/*3 or CYP2C19*2/*3 was estimated as 25.8% from the CV for AUC of omeprazole in PMs. With this, CVs of CL(int,h) in extensive metabolizers (EM: CYP2C19*1/*1), intermediate metabolizers (IM: CYP2C19*1/*2 or *3) and ultra-rapid metabolizers (UM), CYP2C19*17/*17 and *1/*17, were estimated as 66.0%, 55.8%, 6.8% and 48.0%, respectively. To validate these CVs, variability in the AUC of CYP2C19 substrates lansoprazole and rabeprazole, partially metabolized by CYP3A4 in EMs and IMs, were simulated using the CV in CL(int,h) for CYP2C19 EMs and IMs and 33% of the CV previously reported for CYP3A4. Published values were within 2.5-97.5 percentile range of simulated CVs for the AUC. Furthermore, simulated CVs for the AUC of omeprazole and lansoprazole in ungenotyped populations were comparable with published values. Thus, estimated CL(int,h) variability can predict variability in the AUC of drugs metabolized not only by CYP2C19 but also by multiple enzymes.

Fluoxetine- and norfluoxetine-mediated complex drug-drug interactions: in vitro to in vivo correlation of effects on CYP2D6, CYP2C19, and CYP3A4

Fluoxetine and its circulating metabolite norfluoxetine comprise a complex multiple-inhibitor system that causes reversible or time-dependent inhibition of the cytochrome P450 (CYP) family members CYP2D6, CYP3A4, and CYP2C19 in vitro. Although significant inhibition of all three enzymes in vivo was predicted, the areas under the concentration-time curve (AUCs) for midazolam and lovastatin were unaffected by 2-week dosing of fluoxetine, whereas the AUCs of dextromethorphan and omeprazole were increased by 27- and 7.1-fold, respectively. This observed discrepancy between in vitro risk assessment and in vivo drug-drug interaction (DDI) profile was rationalized by time-varying dynamic pharmacokinetic models that incorporated circulating concentrations of fluoxetine and norfluoxetine enantiomers, mutual inhibitor-inhibitor interactions, and CYP3A4 induction. The dynamic models predicted all DDIs with less than twofold error. This study demonstrates that complex DDIs that involve multiple mechanisms, pathways, and inhibitors with their metabolites can be predicted and rationalized via characterization of all the inhibitory species in vitro.

Stereoselective inhibition of CYP2C19 and CYP3A4 by fluoxetine and its metabolite: implications for risk assessment of multiple time-dependent inhibitor systems

Recent guidance on drug-drug interaction (DDI) testing recommends evaluation of circulating metabolites. However, there is little consensus on how to quantitatively predict and/or assess the risk of in vivo DDIs by multiple time-dependent inhibitors (TDIs) including metabolites from in vitro data. Fluoxetine was chosen as the model drug to evaluate the role of TDI metabolites in DDI prediction because it is a TDI of both CYP3A4 and CYP2C19 with a circulating N-dealkylated inhibitory metabolite, norfluoxetine. In pooled human liver microsomes, both enantiomers of fluoxetine and norfluoxetine were TDIs of CYP2C19, (S)-norfluoxetine was the most potent inhibitor with time-dependent inhibition affinity constant (KI) of 7 μM, and apparent maximum time-dependent inhibition rate (k(inact,app)) of 0.059 min(-1). Only (S)-fluoxetine and (R)-norfluoxetine were TDIs of CYP3A4, with (R)-norfluoxetine being the most potent (K(I) = 8 μM, and k(inact,app) = 0.011 min(-1)). Based on in-vitro-to-in-vivo predictions, (S)-norfluoxetine plays the most important role in in vivo CYP2C19 DDIs, whereas (R)-norfluoxetine is most important in CYP3A4 DDIs. Comparison of two multiple TDI prediction models demonstrated significant differences between them in in-vitro-to-in-vitro predictions but not in in-vitro-to-in-vivo predictions. Inclusion of all four inhibitors predicted an in vivo decrease in CYP2C19 (95%) and CYP3A4 (60-62%) activity. The results of this study suggest that adequate worst-case risk assessment for in vivo DDIs by multiple TDI systems can be achieved by incorporating time-dependent inhibition by both parent and metabolite via simple addition of the in vivo time-dependent inhibition rate/cytochrome P450 degradation rate constant (λ/k(deg)) values, but quantitative DDI predictions will require a more thorough understanding of TDI mechanisms.