Risk assessment of mechanism-based inactivation in drug-drug interactions

Physiologically based pharmacokinetic modeling to assess the drug-drug interactions of anaprazole with clarithromycin and amoxicillin in patients undergoing eradication therapy of H. pylori infection

OBJECTIVE

This study aimed to assess the pharmacokinetic (PK) interactions of anaprazole, clarithromycin, and amoxicillin using physiologically based pharmacokinetic (PBPK) models.

METHODS

The PBPK models for anaprazole, clarithromycin, and amoxicillin were constructed using the GastroPlus™ software (Version 9.7) based on the physicochemical data and PK parameters obtained from literature, then were optimized and validated in healthy subjects to predict the plasma concentration-time profiles of these three drugs and assess the predictive performance of each model. According to the analysis of the properties of each drug, the developed and validated models were applied to evaluate potential drug-drug interactions (DDIs) of anaprazole, clarithromycin, and amoxicillin.

RESULTS

The developed PBPK models properly described the pharmacokinetics of anaprazole, clarithromycin, and amoxicillin well, and all predicted PK parameters (Cmax,ss, AUC0-τ,ss) ratios were within 2.0-fold of the observed values. Furthermore, the application of these models to predict the anaprazole-clarithromycin and anaprazole-amoxicillin DDIs demonstrates their good performance, with the predicted DDI Cmax,ss ratios and DDI AUC0-τ,ss ratios within 1.25-fold of the observed values, and all predicted DDI Cmax,ss, and AUC0-τ,ss ratios within 2.0-fold. The simulated results show no need to adjust the dosage when co-administered with anaprazole in patients undergoing eradication therapy of H. pylori infection since the dose remained in the therapeutic range.

CONCLUSION

The whole-body PBPK models of anaprazole, clarithromycin, and amoxicillin were built and qualified, which can predict DDIs that are mediated by gastric pH change and inhibition of metabolic enzymes, providing a mechanistic understanding of the DDIs observed in the clinic of clarithromycin, amoxicillin with anaprazole.

Inhibitory effect of ketoconazole, quinidine and 1-aminobenzotriazole on pharmacokinetics of l-tetrahydropalmatine and its metabolite in rats

l-tetrahydropalmatine (l-THP) is mainly metabolised by CYP450 enzymes.This study was to investigate the possible effect of co-administered CYP inhibitors on the pharmacokinetics of l-THP and its metabolites in rats.An established LC-MS/MS method has been applied for the evaluation of drug-drug interaction between l-THP and CYP inhibitors. Following the administration of CYP inhibitors, a single dose of l-THP (9 mg/kg) was orally administrated.With regard to l-THP, the AUC_0-48 were significantly increased by 4.3, 3.79, and 11.39 folds, and C_max were increased by 4.74, 3.64, and 2.76 folds in the ketoconazole group (KET), quinidine group (QD), and 1-aminobenzotriazole group (ABT), respectively. KET and QD both significantly increased the AUC_0-48 of 2-DM and 2-DM-Glu by 1.38 ∼ 2.43 times, while C_max was significantly decreased by 41.3 and 78.0% in the ABT group, respectively. The C_max of 3-DM was reduced by 51.38, 48.02, and 63.31% after pre-treatment with KET, QD, and ABT, respectively, and C_max of 3-DM-Glu decreased correspondingly by 29.6, 22.1, and 58.0%.Results indicated that CYP inhibitors could markedly influence the systemic level of l-THP and its metabolites. To guarantee the safe use of l-THP, attention should be paid when l-THP was co-administered with CYP inhibitors, particularly with CYP3A4 and 2D6 inhibitors.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A

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.

Ritonavir is the best alternative to ketoconazole as an index inhibitor of cytochrome P450-3A in drug-drug interaction studies

AIMS

The regulatory prohibition of ketoconazole as a CYP3A index inhibitor in drug-drug interaction (DDI) studies has compelled consideration of alternative inhibitors.

METHODS

The biomedical literature was searched to identify DDI studies in which oral midazolam (MDZ) was the victim, and the inhibitory perpetrator was either ketoconazole, itraconazole, clarithromycin, or ritonavir. The ratios (RAUC ) of total area under the curve (AUC) for MDZ with inhibitor divided by MDZ AUC in the control condition were aggregated across individual studies for each inhibitor.

RESULTS

Mean (± SE) RAUC values were: ketoconazole (15 studies, 131 subjects), 11.5 (±1.2); itraconazole (five studies, 48 subjects), 7.3 (±1.0); clarithromycin (five studies, 73 subjects), 6.5 (±10.9); and ritonavir (13 studies, 159 subjects), 14.5 (±2.0). Differences among inhibitors were significant (F = 5.31, P < 0.005). RAUC values were not significantly related to inhibitor dosage or to duration of inhibitor pre-exposure prior to administration of MDZ.

CONCLUSIONS

Ritonavir produces CYP3A inhibition equivalent to or greater than ketoconazole, and is the best index CYP3A inhibitor alternative to ketoconazole. Cobicistat closely resembles ritonavir in structure and function, and can also be considered. Itraconazole and clarithromycin are not suitable alternatives since they do not produce inhibition comparable with ketoconazole or ritonavir, and have other significant disadvantages as well.

Assessment of a candidate marker constituent predictive of a dietary substance-drug interaction: case study with grapefruit juice and CYP3A4 drug substrates

Dietary substances, including herbal products and citrus juices, can perpetrate interactions with conventional medications. Regulatory guidances for dietary substance-drug interaction assessment are lacking. This deficiency is due in part to challenges unique to dietary substances, a lack of requisite human-derived data, and limited jurisdiction. An in vitro-in vivo extrapolation (IVIVE) approach to help address some of these hurdles was evaluated using the exemplar dietary substance grapefruit juice (GFJ), the candidate marker constituent 6',7'-dihydroxybergamottin (DHB), and the purported victim drug loperamide. First, the GFJ-loperamide interaction was assessed in 16 healthy volunteers. Loperamide (16 mg) was administered with 240 ml of water or GFJ; plasma was collected from 0 to 72 hours. Relative to water, GFJ increased the geometric mean loperamide area under the plasma concentration-time curve (AUC) significantly (1.7-fold). Second, the mechanism-based inhibition kinetics for DHB were recovered using human intestinal microsomes and the index CYP3A4 reaction, loperamide N-desmethylation (KI [concentration needed to achieve one-half kinact], 5.0 ± 0.9 µM; kinact [maximum inactivation rate constant], 0.38 ± 0.02 minute(-1)). These parameters were incorporated into a mechanistic static model, which predicted a 1.6-fold increase in loperamide AUC. Third, the successful IVIVE prompted further application to 15 previously reported GFJ-drug interaction studies selected according to predefined criteria. Twelve of the interactions were predicted to within the 25% predefined criterion. Results suggest that DHB could be used to predict the CYP3A4-mediated effect of GFJ. This time- and cost-effective IVIVE approach could be applied to other dietary substance-drug interactions to help prioritize new and existing drugs for more advanced (dynamic) modeling and simulation and clinical assessment.

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.

Characterization of inhibition kinetics of (S)-warfarin hydroxylation by noscapine: implications in warfarin therapy

Noscapine is an antitussive and potential anticancer drug. Clinically significant interactions between warfarin and noscapine have been previously reported. In this study, to provide a basis for warfarin dosage adjustment, the inhibition kinetics of noscapine against warfarin metabolism was characterized. Our enzyme kinetics data obtained from human liver microsomes and recombinant CYP2C9 proteins indicate that noscapine is a competitive inhibitor of the (S)-warfarin 7-hydroxylation reaction by CYP2C9. Interestingly, noscapine also inhibited (S)-warfarin metabolism in a NADPH- and time-dependent manner, and removal of unbound noscapine and its metabolites by ultrafiltration did not reverse inhibition of (S)-warfarin metabolism by noscapine, suggesting mechanism-based inhibition of CYP2C9 by noscapine. Spectral scanning of the reaction between CYP2C9 and noscapine revealed the formation of an absorption spectrum at 458 nm, indicating the formation of a metabolite-intermediate complex. Surprisingly, noscapine is a 2- to 3-fold more efficient inactivator of CYP2C9.2 and CYP2C9.3 variants than it is of the wild type, by unknown mechanisms. Based on the inhibitory kinetic data, (S)-warfarin exposure is predicted to increase up to 7-fold (depending on CYP2C9 genotypes) upon noscapine coadministration, mainly due to mechanism-based inactivation of CYP2C9 by noscapine. Together, these results indicate that mechanism-based inhibition of CYP2C9 by noscapine may dramatically alter pharmacokinetics of warfarin and provide a basis for warfarin dosage adjustment when noscapine is coadministered.

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.

Inhibition of CYP2C19 and CYP3A4 by omeprazole metabolites and their contribution to drug-drug interactions

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.

Covalent inhibition of recombinant human carboxylesterase 1 and 2 and monoacylglycerol lipase by the carbamates JZL184 and URB597

Carboxylesterase type 1 (CES1) and CES2 are serine hydrolases located in the liver and small intestine. CES1 and CES2 actively participate in the metabolism of several pharmaceuticals. Recently, carbamate compounds were developed to inhibit members of the serine hydrolase family via covalent modification of the active site serine. URB597 and JZL184 inhibit fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), respectively; however, carboxylesterases in liver have been identified as a major off-target. We report the kinetic rate constants for inhibition of human recombinant CES1 and CES2 by URB597 and JZL184. Bimolecular rate constants (k(inact)/K(i)) for inhibition of CES1 by JZL184 and URB597 were similar [3.9 (±0.2) × 10(3) M(-1) s(-1) and 4.5 (±1.3) × 10(3) M(-1) s(-1), respectively]. However, k(inact)/K(i) for inhibition of CES2 by JZL184 and URB597 were significantly different [2.3 (±1.3) × 10(2) M(-1) s(-1) and 3.9 (±1.0) × 10(3) M(-1) s(-1), respectively]. Rates of inhibition of CES1 and CES2 by URB597 were similar; however, CES1 and MAGL were more potently inhibited by JZL184 than CES2. We also determined kinetic constants for spontaneous reactivation of CES1 carbamoylated by either JZL184 or URB597 and CES1 diethylphosphorylated by paraoxon. The reactivation rate was significantly slower (4.5×) for CES1 inhibited by JZL184 than CES1 inhibited by URB597. Half-life of reactivation for CES1 carbamoylated by JZL184 was 49 ± 15 h, which is faster than carboxylesterase turnover in HepG2 cells. Together, the results define the kinetics of inhibition for a class of drugs that target hydrolytic enzymes involved in drug and lipid metabolism.