QT time prolongation
Adverse drug events
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Explanations of the substances for patients
We have no additional warnings for the combination of abarelix and ketoconazole. 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 ketoconazole (100%). We do not expect any change in exposure for ketoconazole, 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.
Ketoconazole has a mean oral bioavailability [ F ] of 67%, which is why the maximum plasma levels [Cmax] tend to change with an interaction. The terminal half-life [ t12 ] is rather short at 5 hours and constant plasma levels [ Css ] are reached quickly. The protein binding [ Pb ] is moderately strong at 91.5% and the volume of distribution [ Vd ] is very large at 84 liters, Since the substance has a low hepatic extraction rate of 0.09, displacement from protein binding [Pb] in the context of an interaction can lead to increased exposure. 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 ketoconazole increase serotonergic activity.
|Kiesel & Durán b||0||Ø||Ø|
Rating: According to our knowledge, neither abarelix nor ketoconazole increase anticholinergic activity.
QT time prolongation
Rating: In combination, abarelix and ketoconazole can potentially trigger ventricular arrhythmias of the torsades de pointes type.
General adverse effects
|Side effects||∑ frequency||aba||ket|
|Burning sensation||1.0 %||n.a.||+|
|Adrenal insufficiency||1.0 %||n.a.||+|
|Ventricular arrhythmia||0.0 %||n.a.||0.0|
|Hypersensitivity reaction||0.0 %||n.a.||0.0|
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: Ketoconazole is not known to be proarrhythmic without concomitant use of QT interval-prolonging drugs. We report a woman with coronary artery disease who developed a markedly prolonged QT interval and torsades de pointes (TdP) after taking ketoconazole for treatment of fungal infection. Her QT interval returned to normal upon withdrawal of ketoconazole. Genetic study did not find any mutation in her genes that encode cardiac IKr channel proteins. We postulate that by virtue of its direct blocking action on IKr, ketoconazole alone may prolong QT interval and induce TdP. This calls for attention when ketoconazole is administered to patients with risk factors for acquired long QT syndrome.
Abstract: OBJECTIVE: To investigate the effect of efavirenz on the ketoconazole pharmacokinetics in HIV-infected patients. METHODS: Twelve HIV-infected patients were assigned into a one-sequence, two-period pharmacokinetic interaction study. In phase one, the patients received 400 mg of ketoconazole as a single oral dose on day 1; in phase two, they received 600 mg of efavirenz once daily in combination with 150 mg of lamivudine and 30 or 40 mg of stavudine twice daily on days 2 to 16. On day 16, 400 mg of ketoconazole was added to the regimen as a single oral dose. Ketoconazole pharmacokinetics were studied on days 1 and 16. RESULTS: Pretreatment with efavirenz significantly increased the clearance of ketoconazole by 201%. C(max) and AUC(0-24) were significantly decreased by 44 and 72%, respectively. The T ((1/2)) was significantly shorter by 58%. CONCLUSION: Efavirenz has a strong inducing effect on the metabolism of ketoconazole.
Abstract: AIMS: To investigate the interaction between ketoconazole and darunavir (alone and in combination with low-dose ritonavir), in HIV-healthy volunteers. METHODS: Volunteers received darunavir 400 mg bid and darunavir 400 mg bid plus ketoconazole 200 mg bid, in two sessions (Panel 1), or darunavir/ritonavir 400/100 mg bid, ketoconazole 200 mg bid and darunavir/ritonavir 400/100 mg bid plus ketoconazole 200 mg bid, over three sessions (Panel 2). Treatments were administered with food for 6 days. Steady-state pharmacokinetics following the morning dose on day 7 were compared between treatments. Short-term safety and tolerability were assessed. RESULTS: Based on least square means ratios (90% confidence intervals), during darunavir and ketoconazole co-administration, darunavir area under the curve (AUC(12h)), maximum plasma concentration (C(max)) and minimum plasma concentration (C(min)) increased by 155% (80, 261), 78% (28, 147) and 179% (58, 393), respectively, compared with treatment with darunavir alone. Darunavir AUC(12h), C(max) and C(min) increased by 42% (23, 65), 21% (4, 40) and 73% (39, 114), respectively, during darunavir/ritonavir and ketoconazole co-administration, relative to darunavir/ritonavir treatment. Ketoconazole pharmacokinetics was unchanged by co-administration with darunavir alone. Ketoconazole AUC(12h), C(max) and C(min) increased by 212% (165, 268), 111% (81, 144) and 868% (544, 1355), respectively, during co-administration with darunavir/ritonavir compared with ketoconazole alone. CONCLUSIONS: The increase in darunavir exposure by ketoconazole was lower than that observed previously with ritonavir. A maximum ketoconazole dose of 200 mg day(-1) is recommended if used concomitantly with darunavir/ritonavir, with no dose adjustments for darunavir/ritonavir.
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: All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.