Allongement du temps QT
Événements indésirables médicamenteux
|Gain de poids|
Variantes ✨Pour une évaluation intensive des variantes par ordinateur, veuillez choisir l'abonnement standard payant.
Explications concernant les substances pour les patients
Nous n'avons pas de mise en garde supplémentaire concernant l'association de quétiapine et de asénapine. Veuillez également consulter les informations pertinentes des spécialistes.
Les changements d'exposition rapportés correspondent aux changements de la courbe concentration-temps plasmatique [ AUC ]. Nous n'avons détecté aucun changement dans l'exposition à la quétiapine. Nous ne pouvons actuellement pas estimer l'influence de la asénapine. Nous ne prévoyons aucun changement dans l'exposition à la asénapine, lorsqu'il est associé à la quétiapine (100%).
Les paramètres pharmacocinétiques de la population moyenne sont utilisés comme point de départ pour calculer les changements individuels d'exposition dus aux interactions.
La quétiapine a une faible biodisponibilité orale [ F ] de 9%, c'est pourquoi la concentration plasmatique maximale [Cmax] a tendance à changer fortement avec une interaction. La demi-vie terminale [ t12 ] est assez courte (2.6 heures) et des taux plasmatiques constants [ Css ] sont rapidement atteints. La liaison aux protéines [ Pb ] est modérément forte à 83% et le volume de distribution [ Vd ] est de 55 litres, Étant donné que la substance a un faible taux d'extraction hépatique de 0.13, le déplacement de la liaison aux protéines [Pb] dans le contexte d'une interaction peut entraîner une augmentation de l'exposition. Le métabolisme a lieu via CYP2D6 et CYP3A4, entre autres et le transport actif s'effectue notamment via PGP.
La asénapine a une faible biodisponibilité orale [ F ] de 2%, c'est pourquoi la concentration plasmatique maximale [Cmax] a tendance à changer fortement avec une interaction. La demi-vie terminale [ t12 ] est de 24 heures et des taux plasmatiques constants [ Css ] sont atteints après environ 96 heures. La liaison aux protéines [ Pb ] est modérément forte à 95% et le volume de distribution [ Vd ] est très grand à 1700 litres. Le métabolisme se fait principalement via CYP1A2 et le transport actif s'effectue notamment via UGT1A4.
|Effets sérotoninergiques a||0||Ø||Ø|
Note: À notre connaissance, ni la quétiapine ni la asénapine n'augmentent l'activité sérotoninergique.
|Kiesel & Durán b||2||+||+|
Recommandation: Par mesure de précaution, une attention particulière doit être portée aux symptômes anticholinergiques, en particulier après augmentation de la dose et à de celles situées dans la marge thérapeutique supérieure.
Notation: La quétiapine et la asénapine n'ont qu'un effet modéré sur le système anticholinergique. Le risque de syndrome anticholinergique avec ce médicament est plutôt faible si la dosage est respecté.
Allongement du temps QT
Note: En association, la quétiapine et la asénapine peuvent potentiellement déclencher des arythmies ventriculaires de type torsades de pointes.
Effets indésirables généraux
|Effets secondaires||∑ fréquence||qué||asé|
|Gain de poids||25.2 %||15.5||11.5|
|Hypotension orthostatique||8.4 %||7.0||1.5|
|Augmentation de l'appétit||7.0 %||7.0||n.a.|
Constipation (6.5%): quétiapine
Dyspepsie (4.5%): quétiapine
Tachycardie (6%): quétiapine
Suicidaire (2.5%): asénapine, quétiapine
Neutropénie: asénapine, quétiapine
ALT élevé: quétiapine
GGT élevé: quétiapine
Trouble du rêve: quétiapine
Crise d'épilepsie: asénapine, quétiapine
Syndrome malin des neuroleptiques: asénapine, quétiapine
Dyskinésie tardive: quétiapine
Réaction d'hypersensibilité: asénapine
Réactions cutanées allergiques: quétiapine
Syndrome de Stevens-Johnson: quétiapine
Thromboembolie veineuse: quétiapine
Sur la base de vos réponses et des informations scientifiques, nous évaluons le risque individuel d'effets secondaires indésirables. Ces recommandations sont destinées à conseiller les professionnels et ne se substituent pas à la consultation d'un médecin. Dans la version d'essai (alpha), le risque de toutes les substances n'a pas encore été évalué de manière concluante.
Abstract: No Abstract available
Abstract: Quetiapine is a dibenzothiazepine derivative that has been evaluated for management of patients with the manifestations of psychotic disorders. In pharmacokinetic studies in humans, quetiapine was rapidly absorbed after oral administration, with median time to reach maximum observed plasma concentration ranging from 1 to 2 hours. The absolute bioavailability is unknown, but the relative bioavailability from orally administered tablets compared with a solution was nearly complete. Food has minimal effects on quetiapine absorption. The drug is approximately 83% bound to serum proteins. Single and multiple dose studies have demonstrated linear pharmacokinetics in the clinical dose range (up to 375mg twice daily). The drug is eliminated with a mean terminal half-life of approximately 7 hours. The primary route of elimination is through hepatic metabolism. In vitro studies show that quetiapine is predominantly metabolised by cytochrome P450 (CYP) 3A4. After administration of [14C]quetiapine, approximately 73% of the radioactivity was excreted in the urine and 21% in faeces. Quetiapine accounted for less than 1% of the excreted radioactivity. 11 metabolites formed through hepatic oxidation have been identified. Two were found to be pharmacologically active, but they circulate in plasma at 2 to 12% of the concentration of quetiapine and are unlikely to contribute substantially to the pharmacological effects of the drug. The pharmacokinetics of quetiapine do not appear to be altered by cigarette smoking. Oral clearance declines with age, and was reduced in 2 of 8 patients with hepatic dysfunction but not in patients with renal impairment. Quetiapine has no effect on the in vitro activity of CYP1A2, 2C9, 2C19, 2D6 and 3A4 at clinically relevant concentrations. The lack of effect of quetiapine on hepatic oxidation was confirmed in vivo by the lack of effect of quetiapine on antipyrine disposition. Quetiapine had no effect on serum lithium concentration. Phenytoin and thioridazine increase the clearance of quetiapine, and ketoconazole decreases clearance. No clinically significant effects of cimetidine, haloperidol, risperidone or imipramine on the pharmacokinetics of quetiapine were noted. Quetiapine dosage adjustment, therefore, may be necessary when coadministered with phenytoin, thioridazine or other potent CYP3A4 inducers or inhibitors. The relationship between the therapeutic effects and the plasma concentrations of quetiapine has been investigated in a multicentre clinical trial. There was no statistically significant association between trough plasma quetiapine concentration and clinical response as measured by traditional assessments of psychotic symptom severity. Subsequent clinical studies of the plasma concentration versus effect relationships for quetiapine may help to further define guidelines for dosage regimen design.
Abstract: OBJECTIVE: To study the effect of erythromycin on metabolism of quetiapine in Chinese suffering from schizophrenia. METHODS: Nineteen patients received multiple doses of quetiapine (200 mg, twice daily) with or without co-administered erythromycin (500 mg, three times daily). Blood samples were collected at specified time intervals for determination of plasma concentrations of quetiapine and some of its metabolites. RESULTS: With erythromycin co-administration: for quetiapine, maximal plasma concentration (Cmax), area under concentration-time curve of 0-infinity h (AUC0-infinity) and terminal-phase elimination half-life time (t1/2) increased 68, 129 and 92%, respectively, and clearance (CL) and terminal elimination rate constant (Ke) decreased 52% and 55%, respectively; for quetiapine sulfoxide (QTP-SF), Cmax, AUC0-infinity and AUC ratio decreased 64, 23, and 70%, respectively, and t1/2 increased 211%; for 7-hydroxy-quetiapine (QTP-H), Ke and AUC ratio decreased 61% and 45%, respectively, and t1/2 increased 203%; for 7-hydroxy-N-desalkyl-quetiapine (QTP-ND), Cmax, AUC0-infinity and AUC ratio decreased 36, 40 and 71%, respectively. CONCLUSION: Erythromycin has a noticeable effect on the metabolism of quetiapine. When quetiapine is co-administered with CYP3A inhibitors such as erythromycin, the dosing regimen should be modified according to quetiapine TDM.
Abstract: AIMS: To explore the potential for drug interactions on quetiapine pharmacokinetics using in vitro and in vivo assessments. METHODS: The CYP enzymes responsible for quetiapine metabolite formation were assessed using recombinant expressed CYPs and CYP-selective inhibitors. P-glycoprotein (Pgp) transport was tested in MDCK cells expressing the human MDR1 gene. The effects of CYP3A4 inhibition were evaluated clinically in 12 healthy volunteers that received 25 mg quetiapine before and after 4 days of treatment with ketoconazole 200 mg daily. To assess CYP3A4 induction in vivo, 18 patients with psychiatric disorders were titrated to steady-state quetiapine levels (300 mg twice daily), then titrated to 600 mg daily carbamazepine for 2 weeks. RESULTS: CYP3A4 was found to be responsible for formation of quetiapine sulfoxide and N- and O-desalkylquetiapine and not a Pgp substrate. In the clinical studies, ketoconazole increased mean quetiapine plasma C(max) by 3.35-fold, from 45 to 150 ng ml(-1) (mean C(max) ratio 90% CI 2.51, 4.47) and decreased its clearance (Cl/F) by 84%, from 138 to 22 l h(-1) (mean ratio 90% CI 0.13, 0.20). Carbamazepine decreased quetiapine plasma C(max) by 80%, from 1042 to 205 ng ml(-1) (mean C(max) ratio 90% CI 0.14, 0.28) and increased its clearance 7.5-fold, from 65 to 483 l h(-1) (mean ratio 90% CI 6.04, 9.28). CONCLUSIONS: Cytochrome P450 3A4 is a primary enzyme responsible for the metabolic clearance of quetiapine. Quetiapine pharmacokinetics were affected by concomitant administration of ketoconazole and carbamazepine, and therefore other drugs and ingested natural products that strongly modulate the activity or expression of CYP3A4 would be predicted to change exposure to quetiapine.
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: 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: Many antipsychotic drugs cause QT prolongation, although the effect differs based on the particular drug. We sought to determine the potential for antipsychotic drugs to prolong the QTc interval (>470 ms in men and >480 ms in women) using the Bazett formula in a "real-world" setting by analyzing the electrocardiograms of 1017 patients suffering from schizophrenia. Using logistic regression analysis to calculate the adjusted relative risk (RR), we found that chlorpromazine (RR for 100 mg=1.37, 95% confidence interval (CI)=1.14 to 1.64; p<.005), intravenous haloperidol (RR for 2 mg=1.29, 95% CI=1.18 to 1.43; p<.001), and sultopride (RR for 200 mg=1.45, 95% CI=1.28 to 1.63; p<.001) were associated with an increased risk of QTc prolongation. Levomepromazine also significantly lengthened the QTc interval. The second-generation antipsychotic drugs (i.e., olanzapine, quetiapine, risperidone, and zotepine), mood stabilizers, benzodiazepines, and antiparkinsonian drugs did not prolong the QTc interval. Our results suggest that second-generation antipsychotic drugs are generally less likely than first-generation antipsychotic drugs to produce QTc interval prolongation, which may be of use in clinical decision making concerning the choice of antipsychotic medication.
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: Variability in response to atypical antipsychotic drugs is due to genetic and environmental factors. Cytochrome P450 (CYP) isoforms are implicated in the metabolism of drugs, while the P-glycoprotein transporter (P-gp), encoded by the ABCB1 gene, may influence both the blood and brain drug concentrations. This study aimed to identify the possible associations of CYP and ABCB1 genetic polymorphisms with quetiapine and norquetiapine plasma and cerebrospinal fluid (CSF) concentrations and with response to treatment. Twenty-two patients with schizophrenia receiving 600 mg of quetiapine daily were genotyped for four CYP isoforms and ABCB1 polymorphisms. Quetiapine and norquetiapine peak plasma and CSF concentrations were measured after 4 weeks of treatment. Stepwise multiple regression analysis revealed that ABCB1 3435C > T (rs1045642), 2677G > T (rs2032582) and 1236C > T (rs1128503) polymorphisms predicted plasma quetiapine concentrations, explaining 41% of the variability (p = 0.001). Furthermore, the ABCB1 polymorphisms predicted 48% (p = 0.024) of the variability of the Δ PANSS total score, with the non-carriers of the 3435TT showing higher changes in the score. These results suggest that ABCB1 genetic polymorphisms may be a predictive marker of quetiapine treatment in schizophrenia.
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: PURPOSE: QT prolongation can occur with both first- (FGA) and second-generation antipsychotics (SGA). QT prolongation was identified in an adult patient who presented to the emergency room with schizophrenia, fluid and electrolyte imbalances, and pneumonia. Quetiapine, an SGA, was a component of the pharmacotherapy regimen. Based on the Naranjo adverse drug reaction probability scale rating criteria, a probable causal association was made. METHODS: PubMed and Ovid were searched using the terms antipsychotic, psychotropic, QT interval, corrected QT interval (QTc) prolongation, and quetiapine. References were examined for additional articles related to antipsychotic drugs and the QT interval. DISCUSSION: In this patient, the use of quetiapine was identified as a contributing factor in QT prolongation. Prior QT prolongation was experienced with ziprasidone, another SGA. The antidepressant and dose remained consistent throughout the inpatient course of treatment. Other risk factors in this patient included hypokalemia, dehydration, pneumonia, age, gender, and concurrent usage of an antidepressant. Dual psychiatric diagnoses, preexisting cardiovascular disease, and electrolyte disturbances may increase this risk potential. CONCLUSION: Psychiatric patients may be more at risk of cardiovascular complications, such as QT interval prolongation. The pharmacist can help evaluate risk factors and provide input into the care of all patients, particularly those identified as at risk.
Abstract: The antipsychotic drug quetiapine has been approved for the treatment of unipolar and bipolar depression. The antidepressant activity is considered to be mediated by the active metabolite N-desalkylquetiapine, which is mainly formed by CYP3A4. Little is known about the subsequent elimination of this metabolite. Therefore, this study investigated the possible involvement of cytochrome P450 (P450) enzymes in the metabolism of N-desalkylquetiapine. Screening for and interpretation of metabolites were performed by incubating N-desalkylquetiapine in human liver microsomes (HLM) followed by liquid chromatography-tandem mass spectrometry. The possible involvement of P450 enzymes in N-desalkylquetiapine metabolism was evaluated by coincubation of selective P450 inhibitors in HLM and subsequent experiments with recombinant human P450 enzymes. In HLM experiments, three chromatographic peaks were interpreted as possible metabolites of N-desalkylquetiapine, namely, N-desalkylquetiapine sulfoxide, 7-hydroxy-N-desalkylquetiapine, and an unrecognized metabolite (denoted M3). Inhibition of CYP2D6 (by quinidine) reduced formation of 7-hydroxy-N-desalkylquetiapine by 81%, whereas the CYP3A4 inhibitor ketoconazole inhibited formation of N-desalkylquetiapine sulfoxide and M3 by 65 and 34%, respectively. Inhibitors of CYP1A2, CYP2C9, and CYP2C19 showed only limited changes in metabolite formation. In recombinant systems, 7-hydroxy-N-desalkylquetiapine was exclusively formed by CYP2D6, whereas N-desalkylquetiapine sulfoxide and M3 were formed by both CYP3A4 and CYP2D6. Overall, intrinsic clearance of N-desalkylquetiapine was 12-fold higher by recombinant CYP2D6 relative to CYP3A4. In conclusion, N-desalkylquetiapine is metabolized by both CYP2D6 and CYP3A4 in vitro with preference for the former enzyme. The pharmacologically active metabolite, 7-hydroxy-N-desalkylquetiapine, was exclusively formed by CYP2D6, whereas the two other metabolites were mainly formed by CYP3A4.
Abstract: BACKGROUND: Oral fluid provides a noninvasive method of sample collection. The aim of this study was to obtain oral fluid, plasma, and whole blood from patients prescribed amisulpride, aripiprazole, clozapine, quetiapine, risperidone, or sulpiride and to measure plasma:whole blood and plasma:oral fluid analyte distribution. METHODS: Matched oral fluid, plasma and whole-blood samples were analyzed by liquid chromatography-tandem mass spectrometry. RESULTS: There were 101 sets of samples from 90 (56 male, 34 female) patients (nine prescribed 2 antipsychotics, and one 3). There were ≤ 5 samples for aripiprazole, amisulpride, and sulpiride. There was a good relationship between the plasma and hemolyzed whole-blood concentrations (R > 0.95), with plasma:whole-blood ratios varying between 0.7 (amisulpride) and 1.8 (aripiprazole). Amisulpride plasma and oral fluid concentrations were similar, whereas aripiprazole and dehydroaripiprazole oral fluid concentrations were approximately 8% of those in the plasma, reflecting the weak and strong plasma protein binding of these compounds, respectively. For the other analytes, plasma concentrations were 2-4 times higher than oral fluid concentrations. In general, there was a poor relationship (R = 0.3-0.7) between the plasma and oral fluid concentrations, possibly due to intrapatient saliva pH variation during sample collection. CONCLUSIONS: This work shows that hemolyzed whole-blood samples can be used for therapeutic drug monitoring purposes for the analytes of interest, provided that the plasma:whole-blood ratio is taken into account when interpreting results. For aripiprazole and dehydroaripiprazole, measurements in oral fluid will probably not be feasible. However, the relationship between plasma and oral fluid concentration for amisulpride, clozapine (and norclozapine), quetiapine (and possibly quetiapine metabolites), and risperidone/9-hydroxyrisperidone shows potential for oral fluid analysis.
Abstract: BACKGROUND AND OBJECTIVES: Quetiapine is an atypical antipsychotic drug used to treat schizophrenia and acute episodes of mania. Quetiapine is metabolized by CYP3A enzymes including CYP3A5 and is a substrate of P-glycoprotein, an efflux drug transporter encoded by the ABCB1 gene. We assessed the effects of ABCB1 [c.1236C>T (rs1128503), c.2677G>T/A (rs2032582), c.3435C>T (rs1045642)] and CYP3A5*3 (6986A>G) (rs776746) polymorphisms on the pharmacokinetics of quetiapine in humans. MATERIALS AND METHODS: Forty healthy male individuals were enrolled, and their ABCB1 and CYP3A5 polymorphisms were assessed. After a single dose of 100 mg quetiapine was administered, plasma concentrations of quetiapine were measured for 24 h and pharmacokinetic analysis was carried out. RESULTS: The ABCB1 polymorphisms including c.1236C>T, c.2677G>T/A, and c.3435C>>T did not affect plasma levels of quetiapine, and its pharmacokinetic parameters did not differ among ABCB1 genotype groups. However, the CYP3A5*3 polymorphism significantly affected the plasma level of quetiapine and its pharmacokinetics. The peak plasma concentration of quetiapine was 208.39 ng/ml for CYP3A5*1/*1, 243.46 ng/ml for CYP3A5*1/*3, and 332.94 ng/ml for CYP3A5*3/*3 (P=0.0118). The mean AUC(inf) (area under the time vs. concentration curve from 0 to infinity) value was 627.3, 712.77, and 1045.29 ng h/ml, respectively (P=0.0017). CONCLUSION: The results indicated that the genetic polymorphism of CYP3A5*3 but not ABCB1 significantly influences the plasma level of quetiapine and its pharmacokinetics. These findings suggest that the CYP3A5 genetic polymorphism affects the disposition of quetiapine and provide a plausible explanation for interindividual variation in the disposition of this drug.
Abstract: No Abstract available
Abstract: Quetiapine is an atypical antipsychotic drug with a high permeability, moderate solubility and defined as a Biopharmaceutics Classification System class ll compound. The pharmacokinetics (PK) of the quetiapine immediate-release (IR) formulation has been studied in both adults and children, but the quetiapine extended-release (XR) formulation has only been conducted in adults. The purpose of the current study was to use physiologically based pharmacokinetic modeling (PBPK) quantitatively to predict the PK of the XR formulation in children and adolescents. Using a 'learn and confirm' approach, PBPK models were developed employing in vitro ADME and physicochemical data, clinical PK data of quetiapine IR/XR in adults and clinical PK data of quetiapine IR in children. These models can predict well the effects of CYP3A4 inhibition and induction on the PK of quetiapine, the PK profile of quetiapine IR in children and adults, and the PK profile of quetiapine XR in adults. The AUC and Cmax ratios (children vs adults) for the different age groups were in reasonable agreement with the observed ratios. In addition, the PBPK model predicted that children and adolescents are likely to achieve a similar exposure following administration of either the XR formulation once daily or the IR formulation twice daily at similar total daily doses. The results from the study can help inform dosing regimens in pediatrics using the quetiapine XR formulation.
Abstract: BACKGROUND: To investigate the impact of genetic variability in CYP2D6, CYP3A5, and ABCB1 on steady-state serum concentrations of quetiapine and the active metabolite, N-desalkylquetiapine, in psychiatric patients. METHODS: Measured serum concentrations of quetiapine and N-desalkylquetiapine from patients with biobanked DNA samples were included retrospectively from a routine therapeutic drug monitoring database. The impact of CYP2D6, CYP3A5, and ABCB1 (345C>T) genotypes on dose-adjusted serum concentrations (C/D ratios) of quetiapine and N-desalkylquetiapine was investigated by multivariate mixed model analysis. RESULTS: In total, 289 patients with 633 serum measurements were included. In the multivariate analysis, mean C/D ratio of N-desalkylquetiapine was estimated to be 33% and 22% higher in inherent CYP2D6 poor metabolizers (P = 0.03) and heterozygous extensive metabolizers (P < 0.001), respectively, compared with inherent extensive metabolizers. The ABCB1 3435C>T polymorphism and CYP3A5 genotype had no significant influence on either of the substances in the present material. CONCLUSIONS: Genetic variability in CYP2D6 contributes to the interindividual variability in steady-state serum concentrations of N-desalkylquetiapine. Although the metabolite exhibits relevant pharmacological activity, the quantitative effect of CYP2D6 genotype on serum concentration of N-desalkylquetiapine is probably of limited clinical relevance for quetiapine treatment.
Abstract: Quetiapine fumarate is an antipsychotic drug with poor oral bioavailability (9%) due to first-pass metabolism. Present work is an attempt to improve oral bioavailability of quetiapine fumarate by incorporating in solid lipid nanoparticles (SLN). Six quetiapine fumarate SLN formulations were developed using three different lipids by hot homogenisation followed by ultrasonication. The drug excipient compatibility was studied by differential scanning calorimetry (DSC). Stable quetiapine fumarate SLNs having a mean particle size of 200-250 nm with entrapment efficiency varying in between 80% and 92% were developed. The physical stability of optimized formulation F3 was checked at room temperature for 2 months. Comparative bioavailability studies were conducted in male Wistar rats after oral administration of quetiapine fumarate suspension and SLN formulation. The relative bioavailability of quetiapine fumarate from optimized SLN preparation was increased by 3.71 times when compared with the reference quetiapine fumarate suspension. The obtained results are indicative of SLNs as potential lipid carriers for improving the bioavailability of quetiapine fumarate by minimizing first-pass metabolism.
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.