Prolongación del tiempo QT
Eventos adversos de medicamentos
Variantes ✨Para la evaluación computacionalmente intensiva de las variantes, elija la suscripción estándar paga.
Explicaciones de las sustancias para pacientes.
No existen advertencias adicionales para la combinación de fluoxetina y abarelix. Consulte también la información especializada pertinente.
|Fluoxetina||1 [0.42,19.68] 1,2||1|
Los cambios informados en la exposición corresponden a los cambios en la curva de concentración plasmática-tiempo [ AUC ]. No esperamos ningún cambio en la exposición a fluoxetina, cuando se combina con abarelix (100%). El AUC se encuentra entre 42% y 1968% dependiendo del
Los parámetros farmacocinéticos de la población media se utilizan como punto de partida para calcular los cambios individuales en la exposición debidos a las interacciones.
La fluoxetina tiene una biodisponibilidad oral media [ F ] del 60%, por lo que los niveles plasmáticos máximos [Cmax] tienden a cambiar con una interacción. La vida media terminal [ t12 ] es de 24 horas y se alcanzan niveles plasmáticos constantes [ Css ] después de aproximadamente 96 horas. La unión a proteínas [ Pb ] es moderadamente fuerte al 94.5% y el volumen de distribución [ Vd ] es muy grande a 2275 litros, por eso, con una tasa de extracción hepática media de 0.33, tanto el flujo sanguíneo hepático [Q] como un cambio en la unión a proteínas [Pb] son relevantes. El metabolismo tiene lugar a través de CYP2C19, CYP2C9, CYP2D6 y CYP3A4, entre otros.
Se desconoce la biodisponibilidad de la abarelix. La vida media terminal [ t12 ] es relativamente extensa a las 316.8 horas y los niveles plasmáticos constantes [ Css ] sólo se alcanzan después de más de 1267.2 horas. La unión a proteínas [ Pb ] es 97.5% fuerte. Actualmente, se sigue trabajando en el metabolismo por citocromos.
|Efectos serotoninérgicos a||2||++||Ø|
Recomendación: Como medida de precaución, se deben tener en cuenta los síntomas de sobreestimulación serotoninérgica, especialmente después de aumentar la dosis y en dosis en el rango terapéutico superior.
Clasificación: La fluoxetina modula el sistema serotoninérgico en un grado moderado. El riesgo de síndrome serotoninérgico se puede clasificar como bajo con este medicamento si la dosis se encuentra en el rango habitual. Según nuestro conocimiento, la abarelix no aumenta la actividad serotoninérgica.
|Kiesel & Durán b||1||+||Ø|
Recomendación: Como precaución, se debe prestar atención a los síntomas anticolinérgicos, especialmente después de aumentar la dosis y en dosis en el rango terapéutico superior.
Clasificación: La fluoxetina solo tiene un efecto leve sobre el sistema anticolinérgico. El riesgo de síndrome anticolinérgico con este medicamento es relativamente bajo si la dosis se encuentra en el rango habitual. Según nuestro conocimiento, la abarelix no aumenta la actividad anticolinérgica.
Prolongación del tiempo QT
Clasificación: En combinación, la fluoxetina y la abarelix pueden desencadenar potencialmente arritmias ventriculares del tipo torsades de pointes.
Efectos adversos generales
|Efectos secundarios||∑ frecuencia||flu||aba|
|Pérdida de apetito||10.4 %||10.4||n.a.|
|Sintiéndose nervioso||8.5 %||8.5||n.a.|
Xerostomía (8%): fluoxetina
Temblor (8%): fluoxetina
Mareo (6.5%): fluoxetina
Visión borrosa: fluoxetina
Eritema multiforme: fluoxetina
Tiempo de sangrado prolongado: fluoxetina
Reacción anafiláctica: fluoxetina
Con base en sus respuestas e información científica, evaluamos el riesgo individual de efectos secundarios adversos. Estas recomendaciones están destinadas a asesorar a los profesionales y no sustituyen la consulta con un médico. En la versión de prueba restringida (alfa), el riesgo de todas las sustancias aún no se ha evaluado de manera concluyente.
Abstract: A 48-year-old man presented to the emergency department with confusion, agitation, diaphoresis, and muscle rigidity after beginning treatment with fluoxetine, a serotonin reuptake inhibitor. He had discontinued treatment with tranylcypromine, a monoamine oxidase inhibitor, 2 weeks earlier. The constellation of findings was diagnostic of the serotonin syndrome.
Abstract: Fluoxetine is well absorbed after oral intake, is highly protein bound, and has a large volume of distribution. The elimination half-life of fluoxetine is about 1 to 4 days, while that of its metabolite norfluoxetine ranges from 7 to 15 days. Fluoxetine has a nonlinear pharmacokinetic profile. Therefore, the drug should be used with caution in patients with a reduced metabolic capability (i.e. hepatic dysfunction). In contrast with its effect on the pharmacokinetics of other antidepressants, age does not affect fluoxetine pharmacokinetics. This finding together with the better tolerability profile of fluoxetine (compared with tricyclic antidepressants) makes this drug particularly suitable for use in elderly patients with depression. Furthermore, the pharmacokinetics of fluoxetine are not affected by either obesity or renal impairment. On the basis of results of plasma concentration-clinical response relationship studies, there appears to be a therapeutic window for fluoxetine. Concentrations of fluoxetine plus norfluoxetine above 500 micrograms/L appear to be associated with a poorer clinical response than lower concentrations. Fluoxetine interacts with some other drugs. Concomitant administration of fluoxetine increased the blood concentrations of antipsychotics or antidepressants. The interactions between fluoxetine and lithium, tryptophan and monoamine oxidase inhibitors, in particular, are potentially serious, and can lead to the 'serotonergic syndrome'. This is because of synergistic pharmacodynamic effects and the influence of fluoxetine on the bioavailability of these compounds.
Abstract: BACKGROUND: Substrates and inhibitors of the cytochrome P450 isozyme CYP2D6 have overlapping structural characteristics. Two prototype serotonin uptake inhibitors, sertraline and fluoxetine, share these structural criteria and have been identified as potent inhibitors of CYP2D6 in vitro. The current study was undertaken to investigate whether genetically determined CYP2D6 activity alters the disposition of sertraline or fluoxetine or both. METHODS: Single doses of sertraline (50 mg) and fluoxetine (20 mg) were administered successively to 20 young men with high (extensive metabolizers; n = 10) and low (poor metabolizers; n = 10) CYP2D6 activity. Blood and urine samples were collected for 5 to 7 half-lives and sertraline, desmethylsertraline, fluoxetine, and norfluoxetine were determined by GC and HPLC techniques. RESULTS: Poor metabolizers had significantly greater fluoxetine peak plasma concentrations (Cmax; increases 57%), area under the concentration versus time curve (AUCzero-->infinity; increases 290%), and terminal elimination half-life (increases 216%) compared with extensive metabolizers. The total amount of fluoxetine excreted in the urine during 8 days was almost three times higher in poor metabolizers than in extensive metabolizers (719 versus 225 micrograms; p < 0.05), whereas the total amount of norfluoxetine excreted in urine of poor metabolizers was about half of that of extensive metabolizers (524 versus 1047 micrograms; p < 0.05). Norfluoxetine Cmax and AUCzero-->t were significantly smaller in poor metabolizers (decreases 55% and decreases 53%, respectively), and the partial metabolic clearance of fluoxetine into norfluoxetine was 10 times smaller in this group (4.3 +/- 1.9 versus 0.4 +/- 0.1 L/hr; p < 0.05). No significant differences between extensive and poor metabolizers were found for sertraline and desmethylsertraline pharmacokinetics. CONCLUSION: These data indicate that poor metabolizers accumulate fluoxetine but not sertraline and that CYP2D6 plays an important role in the demethylation of fluoxetine but not of sertraline.
Abstract: No Abstract available
Abstract: AIMS: The study was designed to investigate whether genetically determined CYP2C19 activity affects the metabolism of fluoxetine in healthy subjects. METHODS: A single oral dose of fluoxetine (40 mg) was administrated successively to 14 healthy young men with high (extensive metabolizers, n=8) and low (poor metabolizers, n = 6) CYP2C19 activity. Blood samples were collected for 5-7 half-lives and fluoxetine, and norfluoxetine were determined by reversed-phase high performance liquid chromatography. RESULTS: Poor metabolizers (PMs) showed a mean 46% increase in fluoxetine peak plasma concentrations (Cmax, P < 0.001), 128% increase in area under the concentration vs time curve (AUC(0, infinity), P < 0.001), 113% increase in terminal elimination half-life (t(1/2)) (P < 0.001), and 55% decrease in CLo (P < 0.001) compared with extensive metabolizers (EMs). Mean +/- (s.d) norfluoxetine AUC(0, 192 h) was significantly lower in PMs than that in EMs (1343 +/- 277 vs 2935 +/- 311, P < 0.001). Mean fluoxetine Cmax and AUC(0, infinity) in wild-type homozygotes (CYP2C19*1/CYP2C19*1) were significantly lower than that in PMs (22.4 +/- 3.9 vs 36.7 +/- 8.9, P < 0.001; 732 +/- 42 vs 2152 +/- 492, P < 0.001, respectively). Mean oral clearance in individuals with the wild type homozygous genotype was significantly higher than that in heterozygotes and that in PMs (54.7 +/- 3.4 vs 36.0 +/- 8.7, P < 0.01; 54.7 +/- 3.4 vs 20.6 +/- 6.2, P < 0.001, respectively). Mean norfluoxetine AUC(0, 192 h) in PMs was significantly lower than that in wild type homozygotes (1343 +/- 277 vs 3163 +/- 121, P < 0.05) and that in heterozygotes (1343 +/- 277 vs 2706 +/- 273, P < 0.001), respectively. CONCLUSIONS: The results indicated that CYP2C19 appears to play a major role in the metabolism of fluoxetine, and in particular its N-demethylation among Chinese healthy subjects.
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: The objective of this study was to measure the anticholinergic activity (AA) of medications commonly used by older adults. A radioreceptor assay was used to investigate the AA of 107 medications. Six clinically relevant concentrations were assessed for each medication. Rodent forebrain and striatum homogenate was used with tritiated quinuclidinyl benzilate. Drug-free serum was added to medication and atropine standard-curve samples. For medications that showed detectable AA, average steady-state peak plasma and serum concentrations (C(max)) in older adults were used to estimate relationships between in vitro dose and AA. All results are reported in pmol/mL of atropine equivalents. At typical doses administered to older adults, amitriptyline, atropine, clozapine, dicyclomine, doxepin, L-hyoscyamine, thioridazine, and tolterodine demonstrated AA exceeding 15 pmol/mL. Chlorpromazine, diphenhydramine, nortriptyline, olanzapine, oxybutynin, and paroxetine had AA values of 5 to 15 pmol/mL. Citalopram, escitalopram, fluoxetine, lithium, mirtazapine, quetiapine, ranitidine, and temazepam had values less than 5 pmol/mL. Amoxicillin, celecoxib, cephalexin, diazepam, digoxin, diphenoxylate, donepezil, duloxetine, fentanyl, furosemide, hydrocodone, lansoprazole, levofloxacin, metformin, phenytoin, propoxyphene, and topiramate demonstrated AA only at the highest concentrations tested (patients with above-average C(max) values, who receive higher doses, or are frail may show AA). The remainder of the medications investigated did not demonstrate any AA at the concentrations examined. Psychotropic medications were particularly likely to demonstrate AA. Each of the drug classifications investigated (e.g., antipsychotic, cardiovascular) had at least one medication that demonstrated AA at therapeutic doses. Clinicians can use this information when choosing between equally efficacious medications, as well as in assessing overall anticholinergic burden.
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: BACKGROUND: Cognitive decline is common in Parkinson's disease (PD). Although some of the aetiological factors are known, it is not yet known whether drugs with anticholinergic activity (AA) contribute to this cognitive decline. Such knowledge would provide opportunities to prevent acceleration of cognitive decline in PD. OBJECTIVE: To study whether the use of agents with anticholinergic properties is an independent risk factor for cognitive decline in patients with PD. METHODS: A community-based cohort of patients with PD (n=235) were included and assessed at baseline. They were reassessed 4 and 8 years later. Cognition was assessed using the Mini-Mental State Examination (MMSE). A detailed assessment of the AA of all drugs prescribed was made, and AA was classified according to a standardised scale. Relationships between cognitive decline and AA load and duration of treatment were assessed using bivariate and multivariate statistical analyses. RESULTS: More than 40% used drugs with AA at baseline. During the 8-year follow-up, the cognitive decline was higher in those who had been taking AA drugs (median decline on MMSE 6.5 points) compared with those who had not taken such drugs (median decline 1 point; p=0.025). In linear regression analyses adjusting for age, baseline cognition and depression, significant associations with decline on MMSE were found for total AA load (standardised beta=0.229, p=0.04) as well as the duration of using AA drugs (standardised beta 0.231, p=0.032). CONCLUSION: Our findings suggest that there is an association between anticholinergic drug use and cognitive decline in PD. This may provide an important opportunity for clinicians to avoid increasing progression of cognitive decline by avoiding drugs with AA. Increased awareness by clinicians is required about the classes of drugs that have anticholinergic properties.
Abstract: BACKGROUND/AIMS: The nature and extent of adverse cognitive effects due to the prescription of anticholinergic drugs in older people with and without dementia is unclear. METHODS: We calculated the anticholinergic load (ACL) of medications taken by participants of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing, a cohort of 211 Alzheimer's disease (AD) patients, 133 mild cognitive impairment (MCI) patients and 768 healthy controls (HC) all aged over 60 years. The association between ACL and cognitive function was examined for each diagnostic group (HC, MCI, AD). RESULTS: A high ACL within the HC group was associated with significantly slower response speeds for the Stroop color and incongruent trials. No other significant relationships between ACL and cognition were noted. CONCLUSION: In this large cohort, prescribed anticholinergic drugs appeared to have modest effects upon psychomotor speed and executive function, but not on other areas of cognition in healthy older adults.
Abstract: BACKGROUND: Serotonin syndrome is a rare but serious complication of treatment with serotonergic agents. In its severe manifestations, death can ensue. Early recognition and aggressive management are crucial to mitigating the syndrome. Often the presentation can be subtle and easy to miss. CASE REPORTS: We present 2 cases of serotonin syndrome seen in the psychiatric consultation service of a busy academic hospital. Both patients had favorable outcomes because of early recognition and aggressive management. CONCLUSION: Physicians should carefully consider and rule out the clinical diagnosis of serotonin syndrome when presented with an agitated or confused patient who is taking serotonergic agents.
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.
Abstract: BACKGROUND: Anticholinergic drugs put elderly patients at a higher risk for falls, cognitive decline, and delirium as well as peripheral adverse reactions like dry mouth or constipation. Prescribers are often unaware of the drug-based anticholinergic burden (ACB) of their patients. This study aimed to develop an anticholinergic burden score for drugs licensed in Germany to be used by clinicians at prescribing level. METHODS: A systematic literature search in pubmed assessed previously published ACB tools. Quantitative grading scores were extracted, reduced to drugs available in Germany, and reevaluated by expert discussion. Drugs were scored as having no, weak, moderate, or strong anticholinergic effects. Further drugs were identified in clinical routine and included as well. RESULTS: The literature search identified 692 different drugs, with 548 drugs available in Germany. After exclusion of drugs due to no systemic effect or scoring of drug combinations (n = 67) and evaluation of 26 additional identified drugs in clinical routine, 504 drugs were scored. Of those, 356 drugs were categorised as having no, 104 drugs were scored as weak, 18 as moderate and 29 as having strong anticholinergic effects. CONCLUSIONS: The newly created ACB score for drugs authorized in Germany can be used in daily clinical practice to reduce potentially inappropriate medications for elderly patients. Further clinical studies investigating its effect on reducing anticholinergic side effects are necessary for validation.