Prolongación del tiempo QT
Eventos adversos de medicamentos
|Aumento de peso|
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 oxibutinina y asenapina. Consulte también la información especializada pertinente.
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 oxibutinina, cuando se combina con asenapina (100%). No esperamos ningún cambio en la exposición a asenapina, cuando se combina con oxibutinina (100%).
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 oxibutinina tiene una baja biodisponibilidad oral [ F ] del 100 %, por lo que el nivel plasmático máximo [Cmax] tiende a cambiar fuertemente con una interacción. La vida media terminal [ t12 ] es relativamente corta a las 2 horas y los niveles plasmáticos constantes [ Css ] se alcanzan rápidamente. La unión a proteínas [ Pb ] es moderadamente fuerte al 100 % y el volumen de distribución [ Vd ] es muy grande a 193 litros. El metabolismo tiene lugar principalmente a través de CYP3A4.
La asenapina tiene una baja biodisponibilidad oral [ F ] del 100 %, por lo que el nivel plasmático máximo [Cmax] tiende a cambiar fuertemente 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 100 % y el volumen de distribución [ Vd ] es muy grande a 1700 litros. El metabolismo tiene lugar principalmente a través de CYP1A2 y el transporte activo tiene lugar especialmente a través de UGT1A4.
|Efectos serotoninérgicos a||0||Ø||Ø|
Clasificación: Según nuestro conocimiento, ni la oxibutinina ni la asenapina aumentan la actividad serotoninérgica.
|Kiesel & Durán b||4||+++||+|
Recomendación: El riesgo de efectos secundarios anticolinérgicos como visión borrosa, confusión y temblor aumenta con esta terapia. Si es posible, se debe cambiar la terapia o el paciente debe ser monitoreado de cerca por otros síntomas, como estreñimiento, midriasis e hipovigilancia.
Clasificación: La oxibutinina (severo) y la asenapina (leve) combinadas aumentan la actividad anticolinérgica.
Prolongación del tiempo QT
La asenapina puede aumentar potencialmente el tiempo QT, pero no tenemos conocimiento de arritmias del tipo torsades de pointes. No conocemos ningún potencial de prolongación del intervalo QT de la oxibutinina.
Efectos adversos generales
|Efectos secundarios||∑ frecuencia||oxi||ase|
|Aumento de peso||11.5 %||n.a.||11.5|
|Infección del tracto urinario||5.2 %||5.2||n.a.|
Suicida (2.5%): asenapina
Hipotensión ortostática (1.5%): asenapina
Dolor de cabeza: oxibutinina
Síndrome neuroléptico maligno: asenapina
Angioedema: asenapina, oxibutinina
Reacción de hipersensibilidad: asenapina
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: OBJECTIVE: To assess the potential of anticholinergic drugs as a cause of non-degenerative mild cognitive impairment in elderly people. DESIGN: Longitudinal cohort study. SETTING: 63 randomly selected general practices in the Montpellier region of southern France. PARTICIPANTS: 372 people aged > 60 years without dementia at recruitment. MAIN OUTCOME MEASURES: Anticholinergic burden from drug use, cognitive examination, and neurological assessment. RESULTS: 9.2% of subjects continuously used anticholinergic drugs during the year before cognitive assessment. Compared with non-users, they had poorer performance on reaction time, attention, delayed non-verbal memory, narrative recall, visuospatial construction, and language tasks but not on tasks of reasoning, immediate and delayed recall of wordlists, and implicit memory. Eighty per cent of the continuous users were classified as having mild cognitive impairment compared with 35% of non-users, and anticholinergic drug use was a strong predictor of mild cognitive impairment (odds ratio 5.12, P = 0.001). No difference was found between users and non-users in risk of developing dementia at follow-up after eight years. CONCLUSIONS: Elderly people taking anticholinergic drugs had significant deficits in cognitive functioning and were highly likely to be classified as mildly cognitively impaired, although not at increased risk for dementia. Doctors should assess current use of anticholinergic drugs in elderly people with mild cognitive impairment before considering administration of acetylcholinesterase inhibitors.
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: 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: An assessment of the effects of asenapine on QTc interval in patients with schizophrenia revealed a discrepancy between the results obtained by two different methods: an intersection-union test (IUT) (as recommended in the International Conference on Harmonisation E14 guidance) and an exposure-response (E-R) analysis. Simulations were performed in order to understand and reconcile this discrepancy. Although estimates of the time-matched, placebo-corrected mean change in QTc from baseline (ddQTc) at peak plasma concentrations from the E-R analysis ranged from 2 to 5 ms per dose level, the IUT applied to simulated data from the E-R model yielded maximum ddQTc estimates of 7-10 ms for the various doses of asenapine. These results indicate that the IUT can produce biased estimates that may induce a high false-positive rate in individual thorough QTc trials. In such cases, simulations from an E-R model can aid in reconciling the results from the two methods and may support the use of E-R results as a basis for labeling.
Abstract: The metabolism and excretion of asenapine [(3aRS,12bRS)-5-chloro-2-methyl-2,3,3a,12b-tetrahydro-1H-dibenzo[2,3:6,7]-oxepino [4,5-c]pyrrole (2Z)-2-butenedioate (1:1)] were studied after sublingual administration of [(14)C]-asenapine to healthy male volunteers. Mean total excretion on the basis of the percent recovery of the total radioactive dose was ∼90%, with ∼50% appearing in urine and ∼40% excreted in feces; asenapine itself was detected only in feces. Metabolic profiles were determined in plasma, urine, and feces using high-performance liquid chromatography with radioactivity detection. Approximately 50% of drug-related material in human plasma was identified or quantified. The remaining circulating radioactivity corresponded to at least 15 very polar, minor peaks (mostly phase II products). Overall, >70% of circulating radioactivity was associated with conjugated metabolites. Major metabolic routes were direct glucuronidation and N-demethylation. The principal circulating metabolite was asenapine N(+)-glucuronide; other circulating metabolites were N-desmethylasenapine-N-carbamoyl-glucuronide, N-desmethylasenapine, and asenapine 11-O-sulfate. In addition to the parent compound, asenapine, the principal excretory metabolite was asenapine N(+)-glucuronide. Other excretory metabolites were N-desmethylasenapine-N-carbamoylglucuronide, 11-hydroxyasenapine followed by conjugation, 10,11-dihydroxy-N-desmethylasenapine, 10,11-dihydroxyasenapine followed by conjugation (several combinations of these routes were found) and N-formylasenapine in combination with several hydroxylations, and most probably asenapine N-oxide in combination with 10,11-hydroxylations followed by conjugations. In conclusion, asenapine was extensively and rapidly metabolized, resulting in several regio-isomeric hydroxylated and conjugated metabolites.
Abstract: BACKGROUND AND OBJECTIVE: The effects of hepatic or renal impairment on the pharmacokinetics of atypical antipsychotics are not well understood. Drug exposure may increase in patients with hepatic disease, owing to a reduction of certain metabolic enzymes. The objective of the present study was to study the effects of hepatic or renal impairment on the pharmacokinetics of asenapine and its N-desmethyl and N⁺-glucuronide metabolites. METHODS: Two clinical studies were performed to assess exposure to asenapine, desmethylasenapine and asenapine N⁺-glucuronide in subjects with hepatic or renal impairment. Pharmacokinetic parameters were determined from plasma concentration-time data, using standard noncompartmental methods. The pharmacokinetic variables that were studied included the maximum plasma concentration (C(max)) and the time to reach the maximum plasma concentration (t(max)). Eligible subjects, from inpatient and outpatient clinics, were aged ≥18 years with a body mass index of ≥18 kg/m² and ≤32 kg/m². Sublingual asenapine (Saphris®) was administered as a single 5 mg dose. RESULTS: Thirty subjects participated in the hepatic impairment study (normal hepatic function, n = 8; mild hepatic impairment [Child-Pugh class A], n = 8; moderate hepatic impairment [Child-Pugh class B], n = 8; severe hepatic impairment [Child-Pugh class C], n = 6). Thirty-three subjects were enrolled in the renal impairment study (normal renal function, n = 9; mild renal impairment, n = 8; moderate renal impairment, n = 8; severe renal impairment, n = 8). Asenapine and N-desmethylasenapine exposures were unaltered in subjects with mild or moderate hepatic impairment, compared with healthy controls. Severe hepatic impairment was associated with increased area under the plasma concentration-time curve from time zero to infinity (AUC(∞)) values for total asenapine, N-desmethylasenapine and asenapine N⁺-glucuronide (5-, 3-, and 2-fold, respectively), with slight increases in the C(max) of asenapine but 3- and 2-fold decreases in the C(max) values for N-desmethylasenapine and asenapine N⁺-glucuronide, respectively, compared with healthy controls. The mean AUC(∞) of unbound asenapine was more than 7-fold higher in subjects with severe hepatic impairment than in healthy controls. Mild renal impairment was associated with slight elevations in the AUC(∞) of asenapine compared with healthy controls; alterations observed with moderate and severe renal impairment were marginal. N-desmethylasenapine exposure was only slightly altered by renal impairment. No correlations were observed between exposure and creatinine clearance. CONCLUSION: Severe hepatic impairment (Child-Pugh class C) was associated with pronounced increases in asenapine exposure, but significant increases were not seen with mild (Child-Pugh class A) or moderate (Child-Pugh class B) hepatic impairment, or with any degree of renal impairment. Asenapine is not recommended in patients with severe hepatic impairment; no dose adjustment is needed in patients with mild or moderate hepatic impairment, or in patients with renal impairment.
Abstract: No Abstract available
Abstract: Asenapine is one of the newer atypical antipsychotics on the market. It is a sublingually administered drug that is indicated for the treatment of both schizophrenia and bipolar disorder, and is considered to be safe and well tolerated. Herein, we report a 71-year-old female with a history of bipolar disorder who had ventricular trigemini and experienced a large increase in her QTc interval after starting treatment with asenapine. These changes ceased following withdrawal of asenapine. In this case report, we discuss the importance of cardiac monitoring when switching antipsychotics, even to those that are considered to have low cardiac risk.
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.
Abstract: A highly selective and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay has been described for the determination of asenapine (ASE) in presence of its inactive metabolites-desmethyl asenapine (DMA) and asenapine--glucuronide (ASG). ASE, and ASE 13C-d3, used as internal standard (IS), were extracted from 300 µL human plasma by a simple and precise liquid-liquid extraction procedure using methyl-butyl ether. Baseline separation of ASE from its inactive metabolites was achieved on Chromolith Performance RP(100 mm × 4.6 mm) column using acetonitrile-5.0 mM ammonium acetate-10% formic acid (90:10:0.1, v/v/v) within 4.5 min. Quantitation of ASE was done on a triple quadrupole mass spectrometer equipped with electrospray ionization in the positive mode. The protonated precursor to product ion transitions monitored for ASE and ASE 13C-d3 were286.1 → 166.0 and290.0 → 166.1, respectively. The limit of detection (LOD) and limit of quantitation (LOQ) of the method were 0.0025 ng/mL and 0.050 ng/mL respectively in a linear concentration range of 0.050-20.0 ng/mL for ASE. The intra-batch and inter-batch precision (% CV) and mean relative recovery across quality control levels were ≤ 5.8% and 87.3%, respectively. Matrix effect, evaluated as IS-normalized matrix factor, ranged from 1.03 to 1.05. The stability of ASE under different storage conditions was ascertained in presence of the metabolites. The developed method is much simpler, matrix free, rapid and economical compared to the existing methods. The method was successfully used for a bioequivalence study of asenapine in healthy Indian subjects for the first time.