QT time prolongation
Adverse drug events
|Upper respiratory infection|
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Explanations of the substances for patients
We have no additional warnings for the combination of abarelix and itraconazole. Please also consult the relevant specialist information.
The reported changes in exposure correspond to the changes in the plasma concentration-time curve [ AUC ]. We do not expect any change in exposure for abarelix, when combined with itraconazole (100%). We do not expect any change in exposure for itraconazole, when combined with abarelix (100%).
The pharmacokinetic parameters of the average population are used as the starting point for calculating the individual changes in exposure due to the interactions.
The bioavailability of abarelix is unknown. The terminal half-life [ t12 ] is rather long at 316.8 hours and constant plasma levels [ Css ] are only reached after more than 1267.2 hours. The protein binding [ Pb ] is 97.5% strong. The metabolism via cytochromes is currently still being worked on.
Itraconazole has a mean oral bioavailability [ F ] of 55%, which is why the maximum plasma levels [Cmax] tend to change with an interaction. The terminal half-life [ t12 ] is 21 hours and constant plasma levels [ Css ] are reached after approximately 84 hours. The protein binding [ Pb ] is very strong at 99.8% and the volume of distribution [ Vd ] is very large at 796 liters, which is why, with a mean hepatic extraction rate of 0.44, both liver blood flow [Q] and a change in protein binding [Pb] are relevant. The metabolism mainly takes place via CYP3A4 and the active transport takes place in particular via PGP.
|Serotonergic Effects a||0||Ø||Ø|
Rating: According to our knowledge, neither abarelix nor itraconazole increase serotonergic activity.
|Kiesel & Durán b||0||Ø||Ø|
Rating: According to our knowledge, neither abarelix nor itraconazole increase anticholinergic activity.
QT time prolongation
Rating: In combination, abarelix and itraconazole can potentially trigger ventricular arrhythmias of the torsades de pointes type.
General adverse effects
|Side effects||∑ frequency||aba||itr|
|Upper respiratory infection||8.0 %||n.a.||8.0|
|Peripheral edema||4.0 %||n.a.||4.0|
Abdominal pain (2.9%): itraconazole
Diarrhea (2.9%): itraconazole
Dizziness (2.6%): itraconazole
Fever (2.5%): itraconazole
Fatigue (2.3%): itraconazole
Hearing loss: itraconazole
Heart failure: itraconazole
Hypersensitivity reaction: itraconazole
Pulmonary edema: itraconazole
Based on your answers and scientific information, we assess the individual risk of undesirable side effects. These recommendations are intended to advise professionals and are not a substitute for consultation with a doctor. In the restricted test version (alpha), the risk of all substances has not yet been conclusively assessed.
Abstract: No Abstract available
Abstract: Itraconazole (ITZ) is a potent inhibitor of CYP3A in vivo. However, unbound plasma concentrations of ITZ are much lower than its reported in vitro Ki, and no clinically significant interactions would be expected based on a reversible mechanism of inhibition. The purpose of this study was to evaluate the reasons for the in vitro-in vivo discrepancy. The metabolism of ITZ by CYP3A4 was studied. Three metabolites were detected: hydroxy-itraconazole (OH-ITZ), a known in vivo metabolite of ITZ, and two new metabolites: keto-itraconazole (keto-ITZ) and N-desalkyl-itraconazole (ND-ITZ). OHITZ and keto-ITZ were also substrates of CYP3A4. Using a substrate depletion kinetic approach for parameter determination, ITZ exhibited an unbound K(m) of 3.9 nM and an intrinsic clearance (CLint) of 69.3 ml.min(-1).nmol CYP3A4(-1). The respective unbound Km values for OH-ITZ and keto-ITZ were 27 nM and 1.4 nM and the CLint values were 19.8 and 62.5 ml.min(-1).nmol CYP3A4(-1). Inhibition of CYP3A4 by ITZ, OH-ITZ, keto-ITZ, and ND-ITZ was evaluated using hydroxylation of midazolam as a probe reaction. Both ITZ and OH-ITZ were competitive inhibitors of CYP3A4, with unbound Ki (1.3 nM for ITZ and 14.4 nM for OH-ITZ) close to their respective Km. ITZ, OH-ITZ, keto-ITZ and ND-ITZ exhibited unbound IC50 values of 6.1 nM, 4.6 nM, 7.0 nM, and 0.4 nM, respectively, when coincubated with human liver microsomes and midazolam (substrate concentration < Km). These findings demonstrate that ITZ metabolites are as potent as or more potent CYP3A4 inhibitors than ITZ itself, and thus may contribute to the inhibition of CYP3A4 observed in vivo after ITZ dosing.
Abstract: Itraconazole (ITZ) is metabolized in vitro to three inhibitory metabolites: hydroxy-itraconazole (OH-ITZ), keto-itraconazole (keto-ITZ), and N-desalkyl-itraconazole (ND-ITZ). The goal of this study was to determine the contribution of these metabolites to drug-drug interactions caused by ITZ. Six healthy volunteers received 100 mg ITZ orally for 7 days, and pharmacokinetic analysis was conducted at days 1 and 7 of the study. The extent of CYP3A4 inhibition by ITZ and its metabolites was predicted using this data. ITZ, OH-ITZ, keto-ITZ, and ND-ITZ were detected in plasma samples of all volunteers. A 3.9-fold decrease in the hepatic intrinsic clearance of a CYP3A4 substrate was predicted using the average unbound steady-state concentrations (C(ss,ave,u)) and liver microsomal inhibition constants for ITZ, OH-ITZ, keto-ITZ, and ND-ITZ. Accounting for circulating metabolites of ITZ significantly improved the in vitro to in vivo extrapolation of CYP3A4 inhibition compared to a consideration of ITZ exposure alone.
Abstract: PURPOSE: The objective is to confirm if the prediction of the drug-drug interaction using a physiologically based pharmacokinetic (PBPK) model is more accurate. In vivo Ki values were estimated using PBPK model to confirm whether in vitro Ki values are suitable. METHOD: The plasma concentration-time profiles for the substrate with coadministration of an inhibitor were collected from the literature and were fitted to the PBPK model to estimate the in vivo Ki values. The AUC ratios predicted by the PBPK model using in vivo Ki values were compared with those by the conventional method assuming constant inhibitor concentration. RESULTS: The in vivo Ki values of 11 inhibitors were estimated. When the in vivo Ki values became relatively lower, the in vitro Ki values were overestimated. This discrepancy between in vitro and in vivo Ki values became larger with an increase in lipophilicity. The prediction from the PBPK model involving the time profile of the inhibitor concentration was more accurate than the prediction by the conventional methods. CONCLUSION: A discrepancy between the in vivo and in vitro Ki values was observed. The prediction using in vivo Ki values and the PBPK model was more accurate than the conventional methods.
Abstract: Transporters in proximal renal tubules contribute to the disposition of numerous drugs. Furthermore, the molecular mechanisms of tubular secretion have been progressively elucidated during the past decades. Organic anions tend to be secreted by the transport proteins OAT1, OAT3 and OATP4C1 on the basolateral side of tubular cells, and multidrug resistance protein (MRP) 2, MRP4, OATP1A2 and breast cancer resistance protein (BCRP) on the apical side. Organic cations are secreted by organic cation transporter (OCT) 2 on the basolateral side, and multidrug and toxic compound extrusion (MATE) proteins MATE1, MATE2/2-K, P-glycoprotein, organic cation and carnitine transporter (OCTN) 1 and OCTN2 on the apical side. Significant drug-drug interactions (DDIs) may affect any of these transporters, altering the clearance and, consequently, the efficacy and/or toxicity of substrate drugs. Interactions at the level of basolateral transporters typically decrease the clearance of the victim drug, causing higher systemic exposure. Interactions at the apical level can also lower drug clearance, but may be associated with higher renal toxicity, due to intracellular accumulation. Whereas the importance of glomerular filtration in drug disposition is largely appreciated among clinicians, DDIs involving renal transporters are less well recognized. This review summarizes current knowledge on the roles, quantitative importance and clinical relevance of these transporters in drug therapy. It proposes an approach based on substrate-inhibitor associations for predicting potential tubular-based DDIs and preventing their adverse consequences. We provide a comprehensive list of known drug interactions with renally-expressed transporters. While many of these interactions have limited clinical consequences, some involving high-risk drugs (e.g. methotrexate) definitely deserve the attention of prescribers.
Abstract: The accurate estimation of "in vivo" inhibition constants () of inhibitors and fraction metabolized () of substrates is highly important for drug-drug interaction (DDI) prediction based on physiologically based pharmacokinetic (PBPK) models. We hypothesized that analysis of the pharmacokinetic alterations of substrate metabolites in addition to the parent drug would enable accurate estimation of in vivoandTwenty-four pharmacokinetic DDIs caused by P450 inhibition were analyzed with PBPK models using an emerging parameter estimation method, the cluster Newton method, which enables efficient estimation of a large number of parameters to describe the pharmacokinetics of parent and metabolized drugs. For each DDI, two analyses were conducted (with or without substrate metabolite data), and the parameter estimates were compared with each other. In 17 out of 24 cases, inclusion of substrate metabolite information in PBPK analysis improved the reliability of bothandImportantly, the estimatedfor the same inhibitor from different DDI studies was generally consistent, suggesting that the estimatedfrom one study can be reliably used for the prediction of untested DDI cases with different victim drugs. Furthermore, a large discrepancy was observed between the reported in vitroand the in vitro estimates for some inhibitors, and the current in vivoestimates might be used as reference values when optimizing in vitro-in vivo extrapolation strategies. These results demonstrated that better use of substrate metabolite information in PBPK analysis of clinical DDI data can improve reliability of top-down parameter estimation and prediction of untested DDIs.