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 chlorpromazine and abarelix. 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 chlorpromazine, when combined with abarelix (100%). We do not expect any change in exposure for abarelix, when combined with chlorpromazine (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.
Chlorpromazine has a mean oral bioavailability [ F ] of 45%, which is why the maximum plasma levels [Cmax] tend to change with an interaction. The terminal half-life [ t12 ] is rather long at 30 hours and constant plasma levels [ Css ] are only reached after more than 120 hours. The protein binding [ Pb ] is moderately strong at 94.5%. The metabolism takes place via CYP1A2 and CYP2D6, among others and the active transport takes place in particular via PGP.
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 chlorpromazine nor abarelix increase serotonergic activity.
|Kiesel & Durán b||3||+++||Ø|
Recommendation: As a precaution, attention should be paid to anticholinergic symptoms, especially after increasing the dose and at doses in the upper therapeutic range.
Rating: The chlorpromazine greatly increases anticholinergic activity. According to our knowledge, abarelix does not increase anticholinergic activity.
QT time prolongation
Rating: In combination, chlorpromazine and abarelix can potentially trigger ventricular arrhythmias of the torsades de pointes type.
General adverse effects
|Side effects||∑ frequency||chl||aba|
|Orthostatic hypotension||1.0 %||+||n.a.|
Paralytic ileus: chlorpromazine
Aplastic anemia: chlorpromazine
Hypersensitivity reaction: chlorpromazine
Lupus erythematosus: chlorpromazine
Neuroleptic malignant syndrome: chlorpromazine
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: PURPOSE: To present a case of chlorpromazine-associated torsades de pointes, review established cases of ventricular arrhythmias associated with chlorpromazine, and describe the proarrhythmic characteristics of this drug. DATA SOURCES: Articles identified through a search of MEDLINE and IDIS from January 1966-November 2000 and thorough review of the article bibliographies. Patient cases also were identified from a search of the Food and Drug Administration's Adverse Event Reporting System database (November 1997-March 2001). Cases involving intentional overdoses of chlorpromazine were excluded. RESULTS: In addition to the case reported herein, 12 cases of documented, chlorpromazine-associated ventricular arrhythmias were identified; five had characteristic features of torsades de pointes. Chlorpromazine delayed repolarization and produced electrocardiographic abnormalities; although, whether chlorpromazine induced torsades de pointes through a mechanism of early afterdepolarizations is unclear. Similar to other instances of drug-induced torsades de pointes, concurrent factors such as electrolyte deficiencies may place the patient at increased risk for arrhythmia. CONCLUSIONS: Chlorpromazine can delay repolarization and produce electrocardiographic abnormalities. These can result infrequently in ventricular arrhythmias and torsades de pointes, particularly in patients with confounding factors.
Abstract: Anticholinergic Drug Scale (ADS) scores were previously associated with serum anticholinergic activity (SAA) in a pilot study. To replicate these results, the association between ADS scores and SAA was determined using simple linear regression in subjects from a study of delirium in 201 long-term care facility residents who were not included in the pilot study. Simple and multiple linear regression models were then used to determine whether the ADS could be modified to more effectively predict SAA in all 297 subjects. In the replication analysis, ADS scores were significantly associated with SAA (R2 = .0947, P < .0001). In the modification analysis, each model significantly predicted SAA, including ADS scores (R2 = .0741, P < .0001). The modifications examined did not appear useful in optimizing the ADS. This study replicated findings on the association of the ADS with SAA. Future work will determine whether the ADS is clinically useful for preventing anticholinergic adverse effects.
Abstract: BACKGROUND: Adverse effects of anticholinergic medications may contribute to events such as falls, delirium, and cognitive impairment in older patients. To further assess this risk, we developed the Anticholinergic Risk Scale (ARS), a ranked categorical list of commonly prescribed medications with anticholinergic potential. The objective of this study was to determine if the ARS score could be used to predict the risk of anticholinergic adverse effects in a geriatric evaluation and management (GEM) cohort and in a primary care cohort. METHODS: Medical records of 132 GEM patients were reviewed retrospectively for medications included on the ARS and their resultant possible anticholinergic adverse effects. Prospectively, we enrolled 117 patients, 65 years or older, in primary care clinics; performed medication reconciliation; and asked about anticholinergic adverse effects. The relationship between the ARS score and the risk of anticholinergic adverse effects was assessed using Poisson regression analysis. RESULTS: Higher ARS scores were associated with increased risk of anticholinergic adverse effects in the GEM cohort (crude relative risk [RR], 1.5; 95% confidence interval [CI], 1.3-1.8) and in the primary care cohort (crude RR, 1.9; 95% CI, 1.5-2.4). After adjustment for age and the number of medications, higher ARS scores increased the risk of anticholinergic adverse effects in the GEM cohort (adjusted RR, 1.3; 95% CI, 1.1-1.6; c statistic, 0.74) and in the primary care cohort (adjusted RR, 1.9; 95% CI, 1.5-2.5; c statistic, 0.77). CONCLUSION: Higher ARS scores are associated with statistically significantly increased risk of anticholinergic adverse effects in older patients.
Abstract: OBJECTIVES: To examine the longitudinal relationship between cumulative exposure to anticholinergic medications and memory and executive function in older men. DESIGN: Prospective cohort study. SETTING: A Department of Veterans Affairs primary care clinic. PARTICIPANTS: Five hundred forty-four community-dwelling men aged 65 and older with diagnosed hypertension. MEASUREMENTS: The outcomes were measured using the Hopkins Verbal Recall Test (HVRT) for short-term memory and the instrumental activity of daily living (IADL) scale for executive function at baseline and during follow-up. Anticholinergic medication use was ascertained using participants' primary care visit records and quantified as total anticholinergic burden using a clinician-rated anticholinergic score. RESULTS: Cumulative exposure to anticholinergic medications over the preceding 12 months was associated with poorer performance on the HVRT and IADLs. On average, a 1-unit increase in the total anticholinergic burden per 3 months was associated with a 0.32-point (95% confidence interval (CI)= 0.05-0.58) and 0.10-point (95% CI=0.04-0.17) decrease in the HVRT and IADLs, respectively, independent of other potential risk factors for cognitive impairment, including age, education, cognitive and physical function, comorbidities, and severity of hypertension. The association was attenuated but remained statistically significant with memory (0.29, 95% CI=0.01-0.56) and executive function (0.08, 95% CI=0.02-0.15) after further adjustment for concomitant non-anticholinergic medications. CONCLUSION: Cumulative anticholinergic exposure across multiple medications over 1 year may negatively affect verbal memory and executive function in older men. Prescription of drugs with anticholinergic effects in older persons deserves continued attention to avoid deleterious adverse effects.
Abstract: No Abstract available
Abstract: BACKGROUND: Antipsychotics (APs) have been associated with risk of torsade de Pointes (TdP). This has important public health implications. Therefore, (a) we exploited the public FDA Adverse Event Reporting System (FAERS) to characterize their torsadogenic profile; (b) we collected drug utilization data from 12 European Countries to assess the population exposure over the 2005-2010 period. METHODS: FAERS data (2004-2010) were analyzed based on the following criteria: (1) ≥ 4 cases of TdP/QT abnormalities; (2) Significant Reporting Odds Ratio, ROR [Lower Limit of the 95% confidence interval>1], for TdP/QT abnormalities, adjusted and stratified (Arizona CERT drugs as effect modifiers); (3) ≥ 4 cases of ventricular arrhythmia/sudden cardiac death (VA/SCD); (4) Significant ROR for VA/SCD; (5) Significant ROR, combined by aggregating TdP/QT abnormalities with VA and SCD. Torsadogenic signals were characterized in terms of signal strength: from Group A (very strong torsadogenic signal: all criteria fulfilled) to group E (unclear/uncertain signal: only 2/5 criteria). Consumption data were retrieved from 12 European Countries and expressed as defined daily doses per 1,000 inhabitants per day (DID). RESULTS: Thirty-five antipsychotics met at least one criterium: 9 agents were classified in Group A (amisulpride, chlorpromazine, clozapine, cyamemazine, haloperidol, olanzapine, quetiapine, risperidone, ziprasidone). In 2010, the overall exposure to antipsychotics varied from 5.94 DID (Estonia) to 13.99 (France, 2009). Considerable increment of Group A agents was found in several Countries (+3.47 in France): the exposure to olanzapine increased across all Countries (+1.84 in France) and peaked 2.96 in Norway; cyamemazine was typically used only in France (2.81 in 2009). Among Group B drugs, levomepromazine peaked 3.78 (Serbia); fluphenazine 1.61 (Slovenia). CONCLUSIONS: This parallel approach through spontaneous reporting and drug utilization analyses highlighted drug- and Country-specific scenarios requiring potential regulatory consideration: levomepromazine (Serbia), fluphenazine (Slovenia), olanzapine (across Europe), cyamemazine (France). This synergy should be encouraged to support future pharmacovigilance activities.
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.