|⚠️ Ácido valproico|
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 ácido valproico 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 ácido valproico, cuando se combina con asenapina (100%). No esperamos ningún cambio en la exposición a asenapina, cuando se combina con ácido valproico (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.
Se desconoce la biodisponibilidad de la ácido valproico. La vida media terminal [ t12 ] es de 12.5 horas y se alcanzan niveles plasmáticos constantes [ Css ] después de aproximadamente 50 horas. La ventana terapéutica es estrecha y, por tanto, el margen de seguridad es pequeño. Incluso pequeños cambios en la exposición pueden aumentar el riesgo de toxicidad. La unión a proteínas [ Pb ] es moderadamente fuerte al 85%. El metabolismo tiene lugar a través de CYP2B6 y CYP2C9, entre otros y el transporte activo tiene lugar en parte a través de UGT1A4, UGT1A9 y UGT2B7.
La asenapina tiene una baja biodisponibilidad oral [ F ] del 2%, 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 95% 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 ácido valproico ni la asenapina aumentan 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 asenapina 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. El efecto anticolinérgico de la ácido valproico no es relevante.
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 ácido valproico.
Efectos adversos generales
|Efectos secundarios||∑ frecuencia||áci||ase|
|Aumento de peso||34.1 %||25.5||11.5|
|Sintiéndose nervioso||10.0 %||10.0||n.a.|
|Pérdida de apetito||8.0 %||8.0||n.a.|
Ataxia (8%): ácido valproico
Amnesia (5.5%): ácido valproico
Confusión: ácido valproico
Insomnio: ácido valproico
Parestesia: ácido valproico
Encefalopatía: ácido valproico
Parkinsonismo: ácido valproico
Síndrome neuroléptico maligno: asenapina
Apetito incrementado (6%): ácido valproico
Pérdida de peso: ácido valproico
Disnea (5%): ácido valproico
Nistagmo (4.5%): ácido valproico
Palpitaciones (3%): ácido valproico
Taquicardia (3%): ácido valproico
Hipotensión ortostática (1.5%): asenapina
Suicida (2.5%): asenapina
Alopecia: ácido valproico
Trastornos de las uñas: ácido valproico
Eritema multiforme: ácido valproico
Hipertrofia gingival: ácido valproico
Pancreatitis: ácido valproico
Leucopenia: ácido valproico
Trombocitopenia: ácido valproico
Síndrome mielodisplásico: ácido valproico
Tiempo de sangrado prolongado: ácido valproico
Transaminasas elevadas: ácido valproico
Hepatotoxicidad: ácido valproico
Incontinencia urinaria: ácido valproico
Tubulopatía proximales: ácido valproico
Amenorrea: ácido valproico
Angioedema: ácido valproico, asenapina
Síndrome de DRESS: ácido valproico
Lupus eritematoso: ácido valproico
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: 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: Proximal renal tubular acidosis (RTA) (Type II RTA) is characterized by a defect in the ability to reabsorb HCO(3) in the proximal tubule. This is usually manifested as bicarbonate wastage in the urine reflecting that the defect in proximal tubular transport is severe enough that the capacity for bicarbonate reabsorption in the thick ascending limb of Henle's loop and more distal nephron segments is overwhelmed. More subtle defects in proximal bicarbonate transport likely go clinically unrecognized owing to compensatory reabsorption of bicarbonate distally. Inherited proximal RTA is more commonly autosomal recessive and has been associated with mutations in the basolateral sodium-bicarbonate cotransporter (NBCe1). Mutations in this transporter lead to reduced activity and/or trafficking, thus disrupting the normal bicarbonate reabsorption process of the proximal tubules. As an isolated defect for bicarbonate transport, proximal RTA is rare and is more often associated with the Fanconi syndrome characterized by urinary wastage of solutes like phosphate, uric acid, glucose, amino acids, low-molecular-weight proteins as well as bicarbonate. A vast array of rare tubular disorders may cause proximal RTA but most commonly it is induced by drugs. With the exception of carbonic anhydrase inhibitors which cause isolated proximal RTA, drug-induced proximal RTA is associated with Fanconi syndrome. Drugs that have been recently recognized to cause severe proximal RTA with Fanconi syndrome include ifosfamide, valproic acid and various antiretrovirals such as Tenofovir particularly when given to human immunodeficiency virus patients receiving concomitantly protease inhibitors such as ritonavir or reverse transcriptase inhibitors such as didanosine.
Abstract: No Abstract available
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: No Abstract available
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.
Abstract: Valproic acid (VPA) is an older first-line antiepileptic drug with a complex pharmacokinetic (PK) profile, currently under investigation for several novel neurologic and non-neurologic indications. Our study objective was to design and validate a mechanistic model of VPA disposition in adults and children; and evaluate its predictive performance of drug-drug interactions (DDIs). This study expands upon existing physiologically based pharmacokinetic (PBPK) models for VPA by incorporating UGT enzyme kinetics and an advanced dissolution, absorption, and metabolism (ADAM) model for extended-release (ER) formulation. PBPK models for VPA IR and ER formulations were constructed using Simcyp Simulator (Version 15). First-order absorption was used for the immediate-release (IR) formulation and the ADAM model, including a controlled-release profile, for ER. Data from twenty-one published clinical studies were used to assess model performance. The model accurately predicted the concentration-time profiles of IR formulation for single-dose and steady-state doses ranging from 200mg to 1000mg. Similarly profiles were also simulated for ER formulation after a single-dose and steady-state doses of 500mg and 1000mg, respectively. In addition, simulated PK profiles agreed well with the observed data from studies in which VPA ER formulation was given to pediatric patients and VPA IR formulation to adult patients with cirrhosis. The model was further validated with individual adult data from a Phase I clinical trial consisting of eight cohorts after IV infusion of VPA with doses ranging from 15 to 150mg/kg. Co-administrations of VPA as an enzyme-inhibitor with victim drug phenytoin or lorazepam, as well as a substrate with enzyme inducer carbamazepine or phenobarbital, were simulated with the model to evaluate drug-drug interaction. The simulated serum concentration-time profiles were within the 5th and 95th percentiles, and the majority of the predicted area-under-the-curve (AUC) and peak plasma concentration (C) values were within 25% of the reported average values. The comprehensive VPA PBPK model defined by this study may be used to support dosage regimen optimization to improve the safety and efficacy profile of this agent under different scenarios.
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.