Stereoselective inhibition of CYP2C19 and CYP3A4 by fluoxetine and its metabolite: implications for risk assessment of multiple time-dependent inhibitor systems.
ABSTRACT: 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 ?M, 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 ?M, 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 (?/k(deg)) values, but quantitative DDI predictions will require a more thorough understanding of TDI mechanisms.
Project description:The aim of this study was to evaluate the contribution of metabolites to drug-drug interactions (DDI) using the inhibition of CYP2C19 and CYP3A4 by omeprazole and its metabolites as a model. Of the metabolites identified in vivo, 5-hydroxyomeprazole, 5'-O-desmethylomeprazole, omeprazole sulfone, and carboxyomeprazole had a metabolite to parent area under the plasma concentration-time curve (AUC(m)/AUC(p)) ratio ? 0.25 when either total or unbound concentrations were measured after a single 20-mg dose of omeprazole in a cocktail. All of the metabolites inhibited CYP2C19 and CYP3A4 reversibly. In addition omeprazole, omeprazole sulfone, and 5'-O-desmethylomeprazole were time dependent inhibitors (TDI) of CYP2C19, whereas omeprazole and 5'-O-desmethylomeprazole were found to be TDIs of CYP3A4. The in vitro inhibition constants and in vivo plasma concentrations were used to evaluate whether characterization of the metabolites affected DDI risk assessment. Identifying omeprazole as a TDI of both CYP2C19 and CYP3A4 was the most important factor in DDI risk assessment. Consideration of reversible inhibition by omeprazole and its metabolites would not identify DDI risk with CYP3A4, and with CYP2C19, reversible inhibition values would only identify DDI risk if the metabolites were included in the assessment. On the basis of inactivation data, CYP2C19 and CYP3A4 inhibition by omeprazole would be sufficient to identify risk, but metabolites were predicted to contribute 30-63% to the in vivo hepatic interactions. Therefore, consideration of metabolites may be important in quantitative predictions of in vivo DDIs. The results of this study show that, although metabolites contribute to in vivo DDIs, their relative abundance in circulation or logP values do not predict their contribution to in vivo DDI risk.
Project description: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.
Project description:We explored the association between CYP2C19/3A4 mediated drug-gene-interaction (DGI), drug-drug-interaction (DDI) and drug-drug-gene-interaction (DDGI) and (es)citalopram dispensing course. A cohort study was conducted among adult Caucasians from the Lifelines cohort (167,729 participants) and linked dispensing data from the IADB.nl database as part of the PharmLines Initiative. Exposure groups were categorized into (es)citalopram starters with DGI, DDI and DDGI. The primary outcome was drug switching and/or dose adjustment, and the secondary was early discontinuation after the start of (es)citalopram. Logistic regression modeling was applied to estimate adjusted odd ratios with their confidence interval. We identified 316 (es)citalopram starters with complete CYP2C19/3A4 genetic information. The CYP2C19 IM/PM and CYP3A4 NM combination increased risks of switching and/or dose reduction (OR: 2.75, 95% CI: 1.03-7.29). The higher effect size was achieved by the CYP2C19 IM/PM and CYP3A4 IM combination (OR: 4.38, 95% CI: 1.22-15.69). CYP2C19/3A4 mediated DDIs and DDGIs showed trends towards increased risks of switching and/or dose reduction. In conclusion, a DGI involving predicted decreased CYP2C19 function increases the need for (es)citalopram switching and/or dose reduction which might be enhanced by co-presence of predicted decreased CYP3A4 function. For DDI and DDGI, no conclusions can be drawn from the results.
Project description:Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000?146 min-1, the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000?32 min-1. These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.
Project description:AIMS:Previous studies demonstrated direct correlation between CYP2C19 genotype and BMS-823778 clearance in healthy volunteers. The objective of the present study was to develop a physiologically-based pharmacokinetic (PBPK) model for BMS-823778 and use the model to predict PK and drug-drug interaction (DDI) in virtual populations with multiple polymorphic genes. METHODS:The PBPK model was built and verified using existing clinical data. The verified model was simulated to predict PK of BMS-823778 and significance of DDI with a strong CYP3A4 inhibitor in subjects with various CYP2C19 and UGT1A4 genotypes. RESULTS:The verified PBPK model of BMS-823778 accurately recovered observed PK in different populations. In addition, the model was able to capture the exposure differences between subjects with different CYP2C19 genotypes. PK simulation indicated higher exposures of BMS-823778 in CYP2C19 poor metabolizers who were also devoid of UGT1A4 activity, compared to those with normal UGT1A4 functionality. Moderate DDI with itraconazole was predicted in subjects with wild-type CYP2C19 or UGT1A4. However, in subjects without CYP2C19 or UGT1A4 functionality, significant DDI was predicted when BMS-823778 was coadministered with itraconazole. CONCLUSIONS:A PBPK model was developed using clinical data that accurately predicted human PK in different population with various CYP2C19 phenotypes. Simulations with the verified PBPK model indicated that UGT1A4 was probably an important clearance pathway in CYP2C19 poor metabolizers. DDI with itraconazole is likely to be dependent on the genotypes of CYP2C19 and UGT1A4.
Project description:PURPOSE:Fedratinib (INREBIC®), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug-drug interaction (DDI) potentials for fedratinib under clinical scenarios. METHODS:The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp® (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data. RESULTS:The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates. CONCLUSIONS:The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued.
Project description:Acumapimod, an investigational oral p38 mitogen-activated protein kinase inhibitor for treatment during severe acute exacerbations of chronic obstructive pulmonary disease, is metabolized primarily by cytochrome P450 3A4 (CYP3A4) and is a P-glycoprotein (P-gp) substrate. Concerns about drug-drug interactions (DDIs) have meant patients receiving drugs that inhibit CYP3A4 were ineligible for acumapimod trials. We report on how 2 acumapimod clinical DDI studies and a physiologically-based pharmacokinetic (PBPK) model assessing how co-administration of a weak (azithromycin) and strong (itraconazole) CYP3A4 inhibitor affected acumapimod systemic exposure, informed decision making and supported concomitant use of CYP3A4 and P-gp inhibitors. Studies MBCT102 and MBCT103, respectively, demonstrated that co-administration of azithromycin or itraconazole had no clinically meaningful impact on acumapimod pharmacokinetics. Findings were consistent with PBPK model results. Safety profiles were similar when acumapimod was co-administered with azithromycin or itraconazole. These studies highlight the value of PBPK modeling in drug development, and its potential to inform DDI investigations.
Project description:Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information from the literature is a laborious task. However, text mining can be used to find DDIs in the biomedical literature. Among the recently developed neural networks, we use a Recursive Neural Network to improve the performance of DDI extraction. Our recursive neural network model uses a position feature, a subtree containment feature, and an ensemble method to improve the performance of DDI extraction. Compared with the state-of-the-art models, the DDI detection and type classifiers of our model performed 4.4% and 2.8% better, respectively, on the DDIExtraction Challenge'13 test data. We also validated our model on the PK DDI corpus that consists of two types of DDIs data: in vivo DDI and in vitro DDI. Compared with the existing model, our detection classifier performed 2.3% and 6.7% better on in vivo and in vitro data respectively. The results of our validation demonstrate that our model can automatically extract DDIs better than existing models.
Project description:Induction of intestinal drug metabolizing enzymes can complicate the development of new drugs, owing to the potential to cause drug-drug interactions (DDIs) leading to changes in pharmacokinetics, safety and efficacy. The development of a human-relevant model of the adult intestine that accurately predicts CYP450 induction could help address this challenge as species differences preclude extrapolation from animals. Here, we combined organoids and Organs-on-Chips technology to create a human Duodenum Intestine-Chip that emulates intestinal tissue architecture and functions, that are relevant for the study of drug transport, metabolism, and DDI. Duodenum Intestine-Chip demonstrates the polarized cell architecture, intestinal barrier function, presence of specialized cell subpopulations, and in vivo relevant expression, localization, and function of major intestinal drug transporters. Notably, in comparison to Caco-2, it displays improved CYP3A4 expression and induction capability. This model could enable improved in vitro to in vivo extrapolation for better predictions of human pharmacokinetics and risk of DDIs.
Project description:Some biologics can modulate cytokines that may lead to changes in expression of drug-metabolizing enzymes and cause drug-drug interactions (DDI). DDI potential of TV-1106-an albumin-fused growth hormone (GH)-was investigated. In this study, human blood was exposed to recombinant human growth hormone (rhGH) or TV-1106, followed by isolation of the plasma and its application to human hepatocytes. While the treatment of blood with rhGH increased multiple cytokines, treatment of blood with TV-1106 had no effect on any of the nine cytokines tested. The interleukin (IL)-6 concentration was higher in the rhGH then in the TV-1106-treated plasma (P < .05). While rhGH had little or no effect on CYP1A2 or CYP2C19 mRNA but increased CYP3A4 mRNA twofold, TV-1106 had little or no effect on cytochrome P450 (CYP) mRNAs in hepatocytes. Although the plasma from rhGH-treated blood lowered CYP1A2 activity, the TV-1106 plasma had no effect on CYP activities. The CYP1A2 activity was lower in the rhGH- then in the TV-1106-plasma treated hepatocytes (P < .05). The results indicated that fusing GH with albumin made TV-1106 an unlikely participant of CYP1A2, CYP2C19 or CYP3A4-facilitated, direct or cytokine-driven DDI.