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 petidina 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 petidina, cuando se combina con asenapina (100%). No esperamos ningún cambio en la exposición a asenapina, cuando se combina con petidina (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 petidina tiene una biodisponibilidad oral media [ F ] del 100 %, por lo que los niveles plasmáticos máximos [Cmax] tienden a cambiar con una interacción. La vida media terminal [ t12 ] es relativamente corta a las 5.9 horas y los niveles plasmáticos constantes [ Css ] se alcanzan rápidamente. La unión a proteínas [ Pb ] es relativamente débil al 100 % y el volumen de distribución [ Vd ] es muy grande a 336 litros, por eso, con una tasa de extracción hepática media de 0,9, tanto el flujo sanguíneo hepático [Q] como un cambio en la unión a proteínas [Pb] son relevantes. El metabolismo tiene lugar a través de CYP2B6, CYP2C19 y CYP3A4, entre otros.
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||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 petidina 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 asenapina no aumenta la actividad serotoninérgica.
|Kiesel & Durán b||3||++||+|
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 petidina (moderado) 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 petidina.
Efectos adversos generales
|Efectos secundarios||∑ frecuencia||pet||ase|
|Aumento de peso||11.5 %||n.a.||11.5|
|Hipotensión ortostática||1.5 %||0.01||1.5|
Incautación: petidina, asenapina
Presión intracraneal elevada: petidina
Síndrome neuroléptico maligno: asenapina
Paro cardiaco: petidina
Shock cardiogénico: petidina
Insuficiencia suprarrenal: petidina
Reacción de hipersensibilidad: petidina, asenapina
Paro respiratorio: petidina
Depresion respiratoria: petidina
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: The effect of concurrent cimetidine administration on the disposition of pethidine was investigated in eight healthy male volunteers (18-31 years). The subjects received 70 mg i.v. pethidine HCl doses before and during cimetidine treatment (1200 mg/day p.o.). During cimetidine treatment, pethidine total body clearance (CL) decreased by 22% (0.611 +/- 0.101 [mean +/- s.d.] to 0.474 +/- 0.098 1 kg-1 h, P less than 0.05) and pethidine volume of distribution at steady state (Vss) decreased by 13% (4.79 +/- 0.82 to 4.16 +/- 0.75 l/kg, P less than 0.05). A cimetidine-induced reduction in pethidine oxidation to norpethidine was suggested by a 23% reduction in norpethidine area under the curve from 0 to 24 h (472 +/- 93 to 362 +/- 38 ng ml-1 h, P less than 0.05) and a 29% reduction in peak norpethidine concentration (26.7 +/- 5.3 to 18.9 +/- 1.9 ng/ml, P less than 0.05). There were no significant linear correlations of serum trough cimetidine concentration with percentage reductions in pethidine CL, pethidine Vss, norpethidine AUC (24), or norpethidine peak concentrations. It would appear that the cimetidine-pethidine kinetic interaction may be of sufficient magnitude to be clinically significant. Caution is advised when patients are treated concurrently with these two agents.
Abstract: In order to evaluate the anticholinergic effect of fentanyl and pethidine, the influence of these drugs on the cumulative dose-response curves of carbacholine on the guinea-pig ileum has been investigated. Fentanyl and pethidine displaced the dose-response curve for carbacholine to the right in a parallel fashion, indicating competitive antagonism. Dissociation constants determined by an agonist EC versus antagonist plot were 0.22 mumol/l for fentanyl and 1.4 mumol/l for pethidine. It is concluded that during high-dose fentanyl anaesthesia, fentanyl may bind to muscarinic receptors and thereby produce a central anticholinergic syndrome. An additional finding was that the maximal response to carbacholine increased significantly when combined with pethidine.
Abstract: Meperidine was initially synthesized as an anticholinergic agent but was soon discovered to have analgesic properties. Although meperidine's anticholinergic effects were demonstrated in vivo, the anticholinergic effects on the biliary and renal tracts have not been demonstrated in vivo. Studies have clearly demonstrated that meperidine is no more efficacious in treating biliary or renal tract spasm than comparative mu opioids. The initial studies demonstrating the analgesic efficacy of meperidine were mostly case reports and not double-blind, randomized, controlled trials in specific populations. Subsequent comparative studies failed to demonstrate any advantages of meperidine over comparable doses of other analgesics. Meperidine was portrayed in practice and teaching as having unique clinical advantages. The analgesic effects of meperidine are not pronounced, and, in addition, meperidine use is complicated by unique side effects including serotonergic crisis and normeperidine toxicity. Meperidine's poor efficacy, toxicity, and multiple drug interactions have resulted in a movement to replace meperidine with more efficacious and less toxic opioid analgesics.
Abstract: Physiologically based pharmacokinetic (PBPK) models can be used to predict drug disposition in humans from animal data and the influence of disease or other changes in physiology on the pharmacokinetics of a drug. The potential usefulness of a PBPK model must however be balanced against the considerable effort needed for its development. Proposed methods to simplify PBPK modeling include predicting the necessary tissue:blood partition coefficients (kp) from physicochemical data on the drug instead of determining them in vivo, formal lumping of model compartments, and replacing the various kp values of the organs and tissues by only two values, for "fat" and "lean" tissues, respectively. The aim of this study was to investigate the effects of simplifying complex PBPK models on their ability to predict drug disposition in humans. Arterial plasma concentration curves of fentanyl and pethidine were simulated by means of a number of successively reduced models. Median absolute prediction errors were used to evaluate the performance of each model, in relation to arterial plasma concentration data from clinical studies, and the Wilcoxon matched pairs test was used for comparison of predictions. An originally diffusion-limited model for fentanyl was simplified to perfusion-limitation, and this model was either lumped, reducing 11 organ/tissue compartments to six, or changed to a model based on only two kp values, those of fat (used for fat and lungs) and muscle (used for all other tissues). None of these simplifications appreciably changed the predictions of arterial drug concentrations in the 10 patients. Perfusion-limited models for pethidine were set up using either experimentally determined [Gabrielsson et al. 1986] or theoretically calculated [Davis and Mapleson 1993] kp values, and predictions using the former were found to be significantly better. Lumping of the models did not appreciably change the predictions; however, going from a full set of kp values to only two ("fat" and "lean") had an adverse effect. Using a kp for lungs determined either in rats or indirectly in humans [Persson et al. 1988], i.e., a total of three kp values, improved these predictions. In conclusion, this study strongly suggested that complex PBPK models for lipophilic basic drugs may be considerably reduced with marginal loss of power to predict standard plasma pharmacokinetics in humans. Determination of only two or three kp values instead of a "full" set can mean an important reduction of experimental work to define a basic model. Organs of particular pharmacological or toxicological interest should of course be investigated separately as needed. This study also suggests and applies a simple method for statistical evaluation of the predictions of PBPK models.
Abstract: Meperidine is an opioid analgesic metabolized in the liver by N-demethylation to normeperidine, a potent stimulant of the central nervous system. The purpose of this study was to identify the human cytochrome P450 (P450) enzymes involved in normeperidine formation. Our in vitro studies included 1) screening 16 expressed P450s for normeperidine formation, 2) kinetic experiments on human liver microsomes and candidate P450s, and 3) correlation and inhibition experiments using human hepatic microsomes. After normalization by its relative abundance in human liver microsomes, CYP2B6, CYP3A4, and CYP2C19 accounted for 57, 28, and 15% of the total intrinsic clearance of meperidine. CYP3A5 and CYP2D6 contributed to < 1%. Formation of normeperidine significantly correlated with CYP2B6-selective S-mephenytoin N-demethylation (r = 0.88, p < 0.0001 at 75 > microM meperidine, and r = 0.89, p < 0.0001 at 350 microM meperidine, n = 21) and CYP3A4-selective midazolam 1'-hydroxylation (r = 0.59, p < 0.01 at 75 microM meperidine, and r = 0.55, p < 0.01 at 350 microM meperidine, n = 23). No significant correlation was observed with CYP2C19-selective S-mephenytoin 4'-hydroxylation (r = 0.36, p = 0.2 at 75 microM meperidine, and r = 0.02, p = 0.9 at 350 microM meperidine, n = 13). An anti-CYP2B6 antibody inhibited normeperidine formation by 46%. In contrast, antibodies inhibitory to CYP3A4 and CYP2C8/9/18/19 had little effect (<14% inhibition). Experiments with thiotepa and ketoconazole suggested inhibition of microsomal CYP2B6 and CYP3A4 activity, whereas studies with fluvoxamine (a substrate of CYP2C19) were inconclusive due to lack of specificity. We conclude that normeperidine formation in human liver microsomes is mainly catalyzed by CYP2B6 and CYP3A4, with a minor contribution from CYP2C19.
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.