QT time prolongation
Adverse drug events
|Stevens johnson syndrome|
|Toxic epidermal necrolysis|
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Explanations of the substances for patients
We have no additional warnings for the combination of abarelix and piperacillin. 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 piperacillin (100%). We do not expect any change in exposure for piperacillin, 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.
Piperacillin has a low oral bioavailability [ F ] of 0%, which is why the maximum plasma level [Cmax] tends to change strongly with an interaction. The terminal half-life [ t12 ] is rather short at 0.9 hours and constant plasma levels [ Css ] are reached quickly. The protein binding [ Pb ] is very weak at 30%. About 80.0% of an administered dose is excreted unchanged via the kidneys and this proportion is seldom changed by interactions. The metabolism does not take place via the common cytochromes and the active transport takes place in particular via MRP4.
|Serotonergic Effects a||0||Ø||Ø|
Rating: According to our knowledge, neither abarelix nor piperacillin increase serotonergic activity.
|Kiesel & Durán b||0||Ø||Ø|
Rating: According to our knowledge, abarelix does not increase anticholinergic activity. The anticholinergic effect of piperacillin is not relevant.
QT time prolongation
Rating: In combination, abarelix and piperacillin can potentially trigger ventricular arrhythmias of the torsades de pointes type.
General adverse effects
|Side effects||∑ frequency||aba||pip|
|Erythema multiforme||0.0 %||n.a.||0.01|
|Stevens johnson syndrome||0.0 %||n.a.||0.01|
|Toxic epidermal necrolysis||0.0 %||n.a.||0.01|
|Hemolytic anemia||0.0 %||n.a.||0.01|
|Hypersensitivity reaction||0.0 %||n.a.||0.01|
|Renal failure||0.0 %||n.a.||0.01|
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: BACKGROUND: Drug-induced torsades de pointes (TdP) is a complex regulatory and clinical problem due to the rarity of this sometimes fatal adverse event. In this context, the US FDA Adverse Event Reporting System (AERS) is an important source of information, which can be applied to the analysis of TdP liability of marketed drugs. OBJECTIVE: To critically evaluate the risk of antimicrobial-induced TdP by detecting alert signals in the AERS, on the basis of both quantitative and qualitative analyses. METHODS: Reports of TdP from January 2004 through December 2008 were retrieved from the public version of the AERS. The absolute number of cases and reporting odds ratio as a measure of disproportionality were evaluated for each antimicrobial drug (quantitative approach). A list of drugs with suspected TdP liability (provided by the Arizona Centre of Education and Research on Therapeutics [CERT]) was used as a reference to define signals. In a further analysis, to refine signal detection, we identified TdP cases without co-medications listed by Arizona CERT (qualitative approach). RESULTS: Over the 5-year period, 374 reports of TdP were retrieved: 28 antibacterials, 8 antifungals, 1 antileprosy and 26 antivirals were involved. Antimicrobials more frequently reported were levofloxacin (55) and moxifloxacin (37) among the antibacterials, fluconazole (47) and voriconazole (17) among the antifungals, and lamivudine (8) and nelfinavir (6) among the antivirals. A significant disproportionality was observed for 17 compounds, including several macrolides, fluoroquinolones, linezolid, triazole antifungals, caspofungin, indinavir and nelfinavir. With the qualitative approach, we identified the following additional drugs or fixed dose combinations, characterized by at least two TdP cases without co-medications listed by Arizona CERT: ceftriaxone, piperacillin/tazobactam, cotrimoxazole, metronidazole, ribavirin, lamivudine and lopinavir/ritonavir. DISCUSSION: Disproportionality for macrolides, fluoroquinolones and most of the azole antifungals should be viewed as 'expected' according to Arizona CERT list. By contrast, signals were generated by linezolid, caspofungin, posaconazole, indinavir and nelfinavir. Drugs detected only by the qualitative approach should be further investigated by increasing the sensitivity of the method, e.g. by searching also for the TdP surrogate marker, prolongation of the QT interval. CONCLUSIONS: The freely available version of the FDA AERS database represents an important source to detect signals of TdP. In particular, our analysis generated five signals among antimicrobials for which further investigations and active surveillance are warranted. These signals should be considered in evaluating the benefit-risk profile of these drugs.
Abstract: BACKGROUND: Anticholinergic drugs are often involved in explicit criteria for inappropriate prescribing in older adults. Several scales were developed for screening of anticholinergic drugs and estimation of the anticholinergic burden. However, variation exists in scale development, in the selection of anticholinergic drugs, and the evaluation of their anticholinergic load. This study aims to systematically review existing anticholinergic risk scales, and to develop a uniform list of anticholinergic drugs differentiating for anticholinergic potency. METHODS: We performed a systematic search in MEDLINE. Studies were included if provided (1) a finite list of anticholinergic drugs; (2) a grading score of anticholinergic potency and, (3) a validation in a clinical or experimental setting. We listed anticholinergic drugs for which there was agreement in the different scales. In case of discrepancies between scores we used a reputed reference source (Martindale: The Complete Drug Reference®) to take a final decision about the anticholinergic activity of the drug. RESULTS: We included seven risk scales, and evaluated 225 different drugs. Hundred drugs were listed as having clinically relevant anticholinergic properties (47 high potency and 53 low potency), to be included in screening software for anticholinergic burden. CONCLUSION: Considerable variation exists among anticholinergic risk scales, in terms of selection of specific drugs, as well as of grading of anticholinergic potency. Our selection of 100 drugs with clinically relevant anticholinergic properties needs to be supplemented with validated information on dosing and route of administration for a full estimation of the anticholinergic burden in poly-medicated older adults.
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.