QT time prolongation
Adverse drug events
|Disorder of taste|
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Explanations of the substances for patients
We have no additional warnings for the combination of propafenone and abarelix. Please also consult the relevant specialist information.
|Propafenone||1 [0.39,7.41] 1||1|
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 propafenone, when combined with abarelix (100%). The AUC is between 39% and 741% depending on the CYP2D6
The pharmacokinetic parameters of the average population are used as the starting point for calculating the individual changes in exposure due to the interactions.
Propafenone has a low oral bioavailability [ F ] of 30%, which is why the maximum plasma level [Cmax] tends to change strongly with an interaction. The terminal half-life [ t12 ] is rather short at 3.7 hours and constant plasma levels [ Css ] are reached quickly. The protein binding [ Pb ] is moderately strong at 94.3% and the volume of distribution [ Vd ] is very large at 175 liters. which is why, with a mean hepatic extraction rate of 0.65, both liver blood flow [Q] and a change in protein binding [Pb] are relevant. The metabolism takes place via CYP1A2, CYP2D6 and CYP3A4, among others.
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.
|Serotonergic Effects a||0||Ø||Ø|
Rating: According to our knowledge, neither propafenone nor abarelix increase serotonergic activity.
|Kiesel & Durán b||0||Ø||Ø|
Rating: According to our knowledge, neither propafenone nor abarelix increase anticholinergic activity.
QT time prolongation
Rating: In combination, propafenone and abarelix can potentially trigger ventricular arrhythmias of the torsades de pointes type.
General adverse effects
|Side effects||∑ frequency||pro||aba|
|Chest pain||5.0 %||5.0||n.a.|
|Disorder of taste||5.0 %||5.0||n.a.|
|Atrioventricular block||2.0 %||2.0||n.a.|
Vomiting (2%): propafenone
Abdominal pain: propafenone
Ventricular tachycardia: propafenone
Blurred vision: propafenone
Cholestatic hepatitis: propafenone
Allergic skin reactions like pruritus and rash: propafenone
Lupus erythematosus: propafenone
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: Propafenone is a class 1C antiarrhythmic agent which is administered as a racemate of S(+)- and R(-)-enantiomers. It is well absorbed and is predominantly bound to alpha 1-acid glycoprotein in the plasma. The enantiomers display stereoselective disposition characteristics, the R-enantiomer being cleared more quickly. The hepatic metabolism of propafenone is polymorphic and genetically determined: about 10% of Caucasians have a reduced capacity to hydroxylate the drug. This polymorphic metabolism accounts for the marked interindividual variability in the relationships between dose and concentration, and between concentration and pharmacodynamic effects. During long term administration, the metabolism is saturable in patients with the 'extensive metaboliser' phenotype, leading to accumulation of the parent compound. Propafenone blocks fast inward sodium channels in a frequency-dependent manner, and also has moderate beta-blocking effects. Both the enantiomers and the 5-OH metabolite have a potency to block sodium channels comparable with that of the parent compound. The S-enantiomer is a more potent beta-antagonist than the R-enantiomer. Propafenone typically slows conduction markedly but only modestly prolongs refractoriness. These cardiac effects are determined by the extent of its myocardial accumulation. The drug should be used with caution in patients with serious structural heart disease, as it may cause or aggravate life-threatening arrhythmias. Significant interactions occur when propafenone is coadministered with other drugs. It increases the plasma concentrations of digoxin, warfarin, metoprolol and propranolol as well as enhancing their respective pharmacodynamic effects. Doses of these drugs should therefore be decreased if they are coadministered with propafenone.
Abstract: The pharmacokinetics of oral and i.v. propafenone and its major metabolites have been investigated in 8 healthy subjects. The total body clearance of propafenone was 963 ml/min, the terminal half-life 198 min and its absolute bioavailability was 15.5%. The two active metabolites (5-hydroxypropafenone and N-depropylpropafenone) showed non-linear kinetics in that both the dose-corrected area under the serum concentration-time curve and the amount excreted in the urine were larger after oral dosing. This resulted in considerably higher serum concentrations of the metabolites despite comparable serum concentrations of the parent compound. Thus, the concentration-effect relationship in the same patient may differ after oral and intravenous doses if concentrations of the active metabolite(s) are not taken into consideration. Although the mechanism of the nonlinearity is not clear, the data indicate that it may be due to saturable biliary excretion of the metabolites.
Abstract: Twenty-nine drugs of disparate structures and physicochemical properties were used in an examination of the capability of human liver microsomal lability data ("in vitro T(1/2)" approach) to be useful in the prediction of human clearance. Additionally, the potential importance of nonspecific binding to microsomes in the in vitro incubation milieu for the accurate prediction of human clearance was investigated. The compounds examined demonstrated a wide range of microsomal metabolic labilities with scaled intrinsic clearance values ranging from less than 0.5 ml/min/kg to 189 ml/min/kg. Microsomal binding was determined at microsomal protein concentrations used in the lability incubations. For the 29 compounds studied, unbound fractions in microsomes ranged from 0.11 to 1.0. Generally, basic compounds demonstrated the greatest extent of binding and neutral and acidic compounds the least extent of binding. In the projection of human clearance values, basic and neutral compounds were well predicted when all binding considerations (blood and microsome) were disregarded, however, including both binding considerations also yielded reasonable predictions. Including only blood binding yielded very poor projections of human clearance for these two types of compounds. However, for acidic compounds, disregarding all binding considerations yielded poor predictions of human clearance. It was generally most difficult to accurately predict clearance for this class of compounds; however the accuracy was best when all binding considerations were included. Overall, inclusion of both blood and microsome binding values gave the best agreement between in vivo clearance values and clearance values projected from in vitro intrinsic clearance data.
Abstract: OBJECTIVE: A clinical study on enzyme induction in elderly subjects was performed by investigation of the effect of rifampin (INN, rifampicin) on propafenone disposition. Propafenone was chosen as a model drug because of its complex metabolism that permits the simultaneous in vivo assessment of induction of phase 1 and phase 2 pathways. METHODS: Six extensive metabolizers of CYP2D6 (age, 70.5 +/- 3.5 years) ingested 600 mg rifampin once daily for 9 consecutive days. One day before the first rifampin dose and on the day of the last rifampin dose, each elderly individual received a single intravenous infusion of 70 mg unlabeled propafenone and received a single oral dose of 300 mg deuterated propafenone 2 hours later. Pharmacokinetics and pharmacodynamics of propafenone were compared before and during induction. RESULTS: Maximum QRS prolongation after oral propafenone was decreased significantly by rifampin (18% +/- 5% versus 6% +/- 3%; P < .01). There were no substantial differences in pharmacokinetics and pharmacodynamics of intravenous propafenone during induction. However, bioavailability of propafenone dropped from 30% +/- 24% to 4% +/- 3% (P < .05). After oral propafenone was administered, clearances through N-dealkylation (6 +/- 3 mL/min versus 26 +/- 16 mL/min; P < .05) and glucuronidation (178 +/- 75 mL/min versus 739 +/- 533 mL/min; P < .05), but not 5-hydroxylation, were increased by rifampin, indicating substantial enzyme induction. CONCLUSIONS: Both phase 1 and phase 2 pathways of propafenone metabolism were induced by rifampin in elderly subjects, resulting in a clinically relevant drug interaction.
Abstract: Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd.
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.