Avvisi di avvertenza
Estensione di tempo QT
Effetti avversi del farmaco
Varianti ✨Per la valutazione computazionalmente intensiva delle varianti, scegli l'abbonamento standard a pagamento.
Aree di applicazione
Spiegazioni per i pazienti
Avvisi di avvertenza
La somministrazione di ketoconazolo e alprazolam deve essere evitata.
Concentrazioni elevate di alprazolam - sedazione aumentata / prolungataMeccanismo: il metabolismo di alprazolam avviene in larga misura attraverso il sistema CYP epatico, in particolare tramite CYP3A4. Il ketoconazolo è un potente inibitore di questo isoenzima.
Effetto: secondo le informazioni specialistiche svizzere per alprazolam, l'uso simultaneo di ketoconazolo è controindicato. L'AUC dell'alprazolam è aumentata di 3,19 volte in seguito alla co-somministrazione con ketoconazolo in vivo.
Misure: la combinazione è da evitare. Se la terapia con benzodiazepine è indicata per l'ansiolisi, deve essere selezionata una benzodiazepina dal ketoconazolo il cui metabolismo è mediato in modo meno forte dal CYP3A4 (ad es. Lorazepam o oxazepam).
I cambiamenti nell'esposizione menzionati si riferiscono ai cambiamenti nella curva concentrazione plasmatica-tempo [AUC]. L'esposizione alla alprazolam aumenta al 208%, se combinato con fluoxetina (101%) e ketoconazolo (207%). Questo può portare a un aumento degli effetti collaterali. Non abbiamo rilevato alcun cambiamento nell'esposizione alla fluoxetina, se combinato con alprazolam (100%). Al momento non possiamo stimare l'influenza della ketoconazolo. L'esposizione alla ketoconazolo aumenta al 103%, se combinato con alprazolam (100%) e fluoxetina (103%).
I parametri farmacocinetici della popolazione media sono utilizzati come punto di partenza per il calcolo delle singole variazioni di esposizione dovute alle interazioni.
La alprazolam ha un'elevata biodisponibilità orale [ F ] del 88%, motivo per cui i livelli plasmatici massimi [Cmax] tendono a cambiare poco durante un'interazione. L'emivita terminale [ t12 ] è di 11.7 ore e i livelli plasmatici costanti [ Css ] vengono raggiunti dopo circa 46.8 ore. Il legame proteico [ Pb ] è moderatamente forte al 70.2% e il volume di distribuzione [ Vd ] è di 50 litri nell'intervallo medio, Poiché la sostanza ha una bassa velocità di estrazione epatica di 0,9, lo spostamento dal legame proteico [Pb] nel contesto di un'interazione può aumentare l'esposizione. Il metabolismo avviene principalmente tramite CYP3A4.
La fluoxetina ha una biodisponibilità orale media [ F ] del 60%, motivo per cui i livelli plasmatici massimi [Cmax] tendono a cambiare con un'interazione. L'emivita terminale [ t12 ] è di 24 ore e i livelli plasmatici costanti [ Css ] vengono raggiunti dopo circa 96 ore. Il legame proteico [ Pb ] è moderatamente forte al 94.5% e il volume di distribuzione [ Vd ] è molto grande a 2275 litri, ecco perché, con una velocità di estrazione epatica media di 0,9, sono rilevanti sia il flusso sanguigno epatico [Q] che una variazione del legame proteico [Pb]. Il metabolismo avviene tramite CYP2C19, CYP2C9, CYP2D6 e CYP3A4, tra gli altri.
La ketoconazolo ha una biodisponibilità orale media [ F ] del 67%, motivo per cui i livelli plasmatici massimi [Cmax] tendono a cambiare con un'interazione. L'emivita terminale [ t12 ] è piuttosto breve a 5 ore e i livelli plasmatici costanti [ Css ] vengono raggiunti rapidamente. Il legame proteico [ Pb ] è moderatamente forte al 91.5% e il volume di distribuzione [ Vd ] è molto grande a 84 litri, Poiché la sostanza ha una bassa velocità di estrazione epatica di 0,9, lo spostamento dal legame proteico [Pb] nel contesto di un'interazione può aumentare l'esposizione. Il metabolismo avviene principalmente tramite CYP3A4 e il trasporto attivo avviene in particolare tramite PGP.
|Effetti serotoninergici a||2||Ø||++||Ø|
Raccomandazione: Come misura precauzionale, devono essere presi in considerazione i sintomi della sovrastimolazione serotoninergica, specialmente dopo aver aumentato la dose e alle dosi nell'intervallo terapeutico superiore.
Valutazione: La fluoxetina modula il sistema serotoninergico in misura moderata. Il rischio di una sindrome serotoninergica può essere classificato basso con questo farmaco se il dosaggio rientra nell'intervallo abituale. Secondo le nostre conoscenze, né la alprazolam né la ketoconazolo aumentano l'attività serotoninergica.
|Kiesel & Durán b||1||Ø||+||Ø|
Raccomandazione: A scopo precauzionale, occorre prestare attenzione ai sintomi anticolinergici, soprattutto dopo aver aumentato la dose ea dosi nel range terapeutico superiore.
Valutazione: La fluoxetina ha solo un lieve effetto sul sistema anticolinergico. Il rischio di sindrome anticolinergica con questo farmaco è piuttosto basso se il dosaggio è nel range usuale. Secondo i nostri risultati, né la alprazolam né la ketoconazolo aumentano l'attività anticolinergica.
Estensione di tempo QT
Valutazione: In combinazione, fluoxetina e ketoconazolo possono potenzialmente innescare aritmie ventricolari di tipo torsione di punta. Non conosciamo alcun potenziale di prolungamento dell'intervallo QT per la alprazolam.
Effetti collaterali generali
|Effetti collaterali||∑ frequenza||alp||flu||ket|
|Problema di coordinamento||24.8 %||24.8↑||n.a.||n.a.|
|Compromissione della memoria||24.3 %||24.3↑||n.a.||n.a.|
|Aumento dell'appetito||19.9 %||19.9↑||n.a.||n.a.|
Rinofaringite (19.5%): fluoxetina
Costipazione (17.1%): alprazolam
Diarrea (13%): fluoxetina
Perdita di appetito (10.4%): fluoxetina
Dispepsi (8%): fluoxetina
Disartria (17.1%): alprazolam
Astenia (14%): fluoxetina
Tremore (8%): fluoxetina
Confusione (6%): alprazolam
Aumento di peso (14.9%): alprazolam
Depressione (11.7%): alprazolam
Ansia (9%): fluoxetina
Nervosismo (8.5%): fluoxetina
Effetto rimbalzo: alprazolam
Riduzione della libido (10.2%): alprazolam
Sensazione di bruciore: ketoconazolo
Eruzione cutanea: ketoconazolo
Sindrome di Stevens Johnson: alprazolam
Eritema multiforme: fluoxetina
Insufficienza surrenalica: ketoconazolo
Visione offuscata: fluoxetina
Insufficienza epatica: alprazolam
Aritmia ventricolare: ketoconazolo
Reazione di ipersensibilità: ketoconazolo
Reazione anafilattica: fluoxetina
Tempo di sanguinamento prolungato: fluoxetina
Sulla base delle vostre
Abstract: The influence of fluoxetine on triazolam pharmacokinetics was studied because of changes in diazepam pharmacokinetics reportedly produced by fluoxetine. Twenty-four healthy volunteers received a single 0.25-mg triazolam tablet alone, and another 0.25-mg tablet after 8 days of fluoxetine therapy 60 mg/day. All subjects received these treatments in the same sequence. Several blood samples were drawn from the subjects after the triazolam doses and were assayed by high-performance liquid chromatography (HPLC). Blood samples were drawn immediately before the last three fluoxetine doses to determine the concentration of fluoxetine and its metabolite norfluoxetine, also by HPLC. The pharmacokinetics of triazolam did not change significantly when the tablets were administered after multiple doses of fluoxetine. These results indicate that no pharmacokinetic interaction exists between triazolam and fluoxetine or norfluoxetine. However, each patient's clinical response to therapy should be monitored when triazolam tablets and fluoxetine capsules are administered concomitantly.
Abstract: Alprazolam is a short-acting triazolobenzodiazepine with anxiolytic and antidepressant properties. It has a half-life of 10-15 hours after multiple oral doses. Approximately 20% of an oral dose is excreted unchanged in the urine. The major urinary metabolites are alpha-OH alprazolam glucuronide and 3-HMB benzophenone glucuronide. The objective of this study was to characterize the reactivity of alprazolam and three metabolites in the Abbott ADx and TDx urinary benzodiazepine assays compared with the EMIT d.a.u. benzodiazepine assay. Alprazolam (at 300 ng/mL) gave an equivalent response as the 300 ng/mL low control (nordiazepam). alpha-OH alprazolam gave an equivalent response to this control between 300-500 ng/mL and 4-OH alprazolam between 500-1000 ng/mL. The 3-HMB benzophenone was not positive even at 10,000 ng/mL. The ADx screening assay was positive in 26 of 31 urine specimens collected from alprazolam-treated patients. All 31 of these specimens were confirmed positive for alpha-OH alprazolam by GC/MS after enzymatic hydrolysis and formation of a TMS derivative. For the TDx, 27 of 31 specimens were positive for benzodiazepines and all 31 were confirmed by GC/MS. All 5 of the negative ADx specimens and 4 of 5 TDx specimens contained 150-400 ng/mL of alpha-OH alprazolam. In conclusion, both the ADx and TDx urine benzodiazepine assays are acceptable screening assays for alprazolam use when the alpha-OH alprazolam concentration is greater than 400 ng/mL.
Abstract: Alprazolam, a triazolobenzodiazepine, is the first of this new class of benzodiazepine drugs to be marketed in the United States and Canada. It achieves peak serum levels in 0.7 to 2.1 hours and has a serum half-life of 12 to 15 hours. When given in the recommended daily dosage of 0.5 to 4.0 mg, it is as effective as diazepam and chlordiazepoxide as an anxiolytic agent. Its currently approved indication is for the treatment of anxiety disorders and symptoms of anxiety, including anxiety associated with depression. Although currently not approved for the treatment of depressive disorders, studies published to date have demonstrated that alprazolam compares favorably with standard tricyclic antidepressants. Also undergoing investigation is the potential role of alprazolam in the treatment of panic disorders. Alprazolam has been used in elderly patients with beneficial results and a low frequency of adverse reactions. Its primary side effect, drowsiness, is less than that produced by diazepam at comparable doses. Data on toxicity, tolerance, and withdrawal profile are limited, but alprazolam seems to be at least comparable to other benzodiazepines. Drug interaction data are also limited, and care should be exercised when prescribing alprazolam for patients taking other psychotropic drugs because of potential additive depressant effects.
Abstract: Six fasting male subjects (20-32 years of age) received an oral tablet and an IV 1.0-mg dose of alprazolam in a crossover-design study. Alprazolam plasma concentration in multiple samples during 36 h after dosing was determined by electron-capture gas-liquid chromatography. Psychomotor performance tests, digit-symbol substitution (DSS), and perceptual speed (PS) were administered at 0, 1.25, 2.25, 5.0, and 12.5 h. Sedation was assessed by the subjects and by an observer using the Stanford Sleepiness Scale and a Nurse Rating Sedation Scale (NRSS), respectively. Mean kinetic parameters after IV and oral alprazolam were as follows: volume of distribution (Vd) 0.72 and 0.84 l/kg; elimination half-life (t1/2) 11.7 and 11.8 h; clearance (Cl) 0.74 and 0.89 ml/min/kg. There were no significant differences between IV and oral alprazolam in Vd, t1/2, or area under the curve. The mean fraction absorbed after oral administration was 0.92. Performance on PS and DSS tests was impaired at 1.25 and 2.5 h, but had returned to baseline at 5.0 h for both treatments. Onset of sedation was rapid after IV administration and the average time of peak sedation was 0.48 h. Sedation scores were significantly lower during hour 1 after oral administration than after IV, but were not significantly different at later times. Alprazolam is fully available after oral administration and kinetic parameters are not affected by route of administration. With the exception of rapidity of onset, the pharmacodynamic profiles of IV and oral alprazolam are very similar after a 1.0-mg dose.
Abstract: A 48-year-old man presented to the emergency department with confusion, agitation, diaphoresis, and muscle rigidity after beginning treatment with fluoxetine, a serotonin reuptake inhibitor. He had discontinued treatment with tranylcypromine, a monoamine oxidase inhibitor, 2 weeks earlier. The constellation of findings was diagnostic of the serotonin syndrome.
Abstract: Fluoxetine is well absorbed after oral intake, is highly protein bound, and has a large volume of distribution. The elimination half-life of fluoxetine is about 1 to 4 days, while that of its metabolite norfluoxetine ranges from 7 to 15 days. Fluoxetine has a nonlinear pharmacokinetic profile. Therefore, the drug should be used with caution in patients with a reduced metabolic capability (i.e. hepatic dysfunction). In contrast with its effect on the pharmacokinetics of other antidepressants, age does not affect fluoxetine pharmacokinetics. This finding together with the better tolerability profile of fluoxetine (compared with tricyclic antidepressants) makes this drug particularly suitable for use in elderly patients with depression. Furthermore, the pharmacokinetics of fluoxetine are not affected by either obesity or renal impairment. On the basis of results of plasma concentration-clinical response relationship studies, there appears to be a therapeutic window for fluoxetine. Concentrations of fluoxetine plus norfluoxetine above 500 micrograms/L appear to be associated with a poorer clinical response than lower concentrations. Fluoxetine interacts with some other drugs. Concomitant administration of fluoxetine increased the blood concentrations of antipsychotics or antidepressants. The interactions between fluoxetine and lithium, tryptophan and monoamine oxidase inhibitors, in particular, are potentially serious, and can lead to the 'serotonergic syndrome'. This is because of synergistic pharmacodynamic effects and the influence of fluoxetine on the bioavailability of these compounds.
Abstract: BACKGROUND: Substrates and inhibitors of the cytochrome P450 isozyme CYP2D6 have overlapping structural characteristics. Two prototype serotonin uptake inhibitors, sertraline and fluoxetine, share these structural criteria and have been identified as potent inhibitors of CYP2D6 in vitro. The current study was undertaken to investigate whether genetically determined CYP2D6 activity alters the disposition of sertraline or fluoxetine or both. METHODS: Single doses of sertraline (50 mg) and fluoxetine (20 mg) were administered successively to 20 young men with high (extensive metabolizers; n = 10) and low (poor metabolizers; n = 10) CYP2D6 activity. Blood and urine samples were collected for 5 to 7 half-lives and sertraline, desmethylsertraline, fluoxetine, and norfluoxetine were determined by GC and HPLC techniques. RESULTS: Poor metabolizers had significantly greater fluoxetine peak plasma concentrations (Cmax; increases 57%), area under the concentration versus time curve (AUCzero-->infinity; increases 290%), and terminal elimination half-life (increases 216%) compared with extensive metabolizers. The total amount of fluoxetine excreted in the urine during 8 days was almost three times higher in poor metabolizers than in extensive metabolizers (719 versus 225 micrograms; p < 0.05), whereas the total amount of norfluoxetine excreted in urine of poor metabolizers was about half of that of extensive metabolizers (524 versus 1047 micrograms; p < 0.05). Norfluoxetine Cmax and AUCzero-->t were significantly smaller in poor metabolizers (decreases 55% and decreases 53%, respectively), and the partial metabolic clearance of fluoxetine into norfluoxetine was 10 times smaller in this group (4.3 +/- 1.9 versus 0.4 +/- 0.1 L/hr; p < 0.05). No significant differences between extensive and poor metabolizers were found for sertraline and desmethylsertraline pharmacokinetics. CONCLUSION: These data indicate that poor metabolizers accumulate fluoxetine but not sertraline and that CYP2D6 plays an important role in the demethylation of fluoxetine but not of sertraline.
Abstract: No Abstract available
Abstract: AIMS: The study was designed to investigate whether genetically determined CYP2C19 activity affects the metabolism of fluoxetine in healthy subjects. METHODS: A single oral dose of fluoxetine (40 mg) was administrated successively to 14 healthy young men with high (extensive metabolizers, n=8) and low (poor metabolizers, n = 6) CYP2C19 activity. Blood samples were collected for 5-7 half-lives and fluoxetine, and norfluoxetine were determined by reversed-phase high performance liquid chromatography. RESULTS: Poor metabolizers (PMs) showed a mean 46% increase in fluoxetine peak plasma concentrations (Cmax, P < 0.001), 128% increase in area under the concentration vs time curve (AUC(0, infinity), P < 0.001), 113% increase in terminal elimination half-life (t(1/2)) (P < 0.001), and 55% decrease in CLo (P < 0.001) compared with extensive metabolizers (EMs). Mean +/- (s.d) norfluoxetine AUC(0, 192 h) was significantly lower in PMs than that in EMs (1343 +/- 277 vs 2935 +/- 311, P < 0.001). Mean fluoxetine Cmax and AUC(0, infinity) in wild-type homozygotes (CYP2C19*1/CYP2C19*1) were significantly lower than that in PMs (22.4 +/- 3.9 vs 36.7 +/- 8.9, P < 0.001; 732 +/- 42 vs 2152 +/- 492, P < 0.001, respectively). Mean oral clearance in individuals with the wild type homozygous genotype was significantly higher than that in heterozygotes and that in PMs (54.7 +/- 3.4 vs 36.0 +/- 8.7, P < 0.01; 54.7 +/- 3.4 vs 20.6 +/- 6.2, P < 0.001, respectively). Mean norfluoxetine AUC(0, 192 h) in PMs was significantly lower than that in wild type homozygotes (1343 +/- 277 vs 3163 +/- 121, P < 0.05) and that in heterozygotes (1343 +/- 277 vs 2706 +/- 273, P < 0.001), respectively. CONCLUSIONS: The results indicated that CYP2C19 appears to play a major role in the metabolism of fluoxetine, and in particular its N-demethylation among Chinese healthy subjects.
Abstract: We attempted to predict the in vivo metabolic clearance of alprazolam from in vitro metabolic studies using human liver microsomes and human CYP recombinants. Good correlations were observed between the intrinsic clearance (CL(int)) for 4-hydroxylation and CYP3A4 content and between the CL(int) for alpha-hydroxylation and CYP3A5 content in ten human liver microsomal samples. Using the recombinant CYP isoforms expressed in insect cells, the CL(int) for CYP3A4 was about 2-fold higher than the CL(int) for CYP3A5 in the case of 4-hydroxylation. However, the CL(int) for CYP3A5 was about 3-fold higher than the CL(int) for CYP3A4 in the case of alpha-hydroxylation. The metabolic rates for 4- and alpha-hydroxylation increased as the added amount of cytochrome b(5) increased, and their maximum values were 3- to 4-fold higher than those without cytochrome b(5). The values of CL(int), in vivo predicted from in vitro studies using human liver microsomes and CYP3A4 and CYP3A5 recombinants were within 2.5 times of the observed value calculated from literature data. The average CL(int) value (sum of 4- and alpha-hydroxylation) obtained using three human liver microsomal samples was 4-fold higher than that obtained using three small intestinal microsomal samples from the same donors, indicating the minor contribution of intestinal metabolism to alprazolam disposition. The area under the plasma concentration-time curve (AUC) of alprazolam is reported to increase following co-administration of ketoconazole and the magnitude of the increase predicted from the in vitro K(i) values and reported pharmacokinetic parameters of ketoconazole was 2.30-2.45, which is close to the value observed in vivo (3.19). A quantitative prediction of the AUC increase by cimetidine was also successful (1.73-1.79 vs 1.58-1.64), considering the active transport of cimetidine into the liver. In conclusion, we have succeeded in carrying out an in vitro/in vivo scaling of alprazolam metabolism using human liver microsomes and human CYP3A4 and CYP3A5 recombinants.
Abstract: Cytochrome P450(CYP)3A4 is one of the CYP enzymes catalyzing oxidative metabolism, and involved in the metabolism of many drugs. Among benzodiazepines, alprazolam, triazolam, brotizolam and midazolam are mainly metabolished by CYP3A4, and quazepam, diazepam and flunitrazepam are partly metabolised by this enzyme. Azole antifungals, macrolide antibiotics, calcium antagonists and grapefruit juice inhibit CYP3A4 activity, while antiepileptics and rifampicin induce the activity. The drugs affecting CYP3A4 activity inhibit or induce the metabolism of the benzodiazepines metabolised by this enzyme, and induce side effects or reduce therapeutic effects of these drugs. Therefore, the combination of the two groups of drugs should be avoided, and if it is unavoidable the dose of benzodiazepines should be adjusted.
Abstract: The objective of this study was to investigate pharmacokinetic and pharmacodynamic interactions between midazolam and fluoxetine, fluvoxamine, nefazodone, and ketoconazole. Forty healthy subjects were randomized to receive one of the four study drugs for 12 days in a parallel study design: fluoxetine 60 mg per day for 5 days, followed by 20 mg per day for 7 days; fluvoxamine titrated to a daily dose of 200 mg; nefazodone titrated to a daily dose of 400 mg; or ketoconazole 200 mg per day. All 40 subjects received oral midazolam solution before and after the 12-day study drug regimen. Blood samples for determination of midazolam concentrations were drawn for 24 hours after each midazolam dose and used for the calculation of pharmacokinetic parameters. The effects of the study drugs on midazolam pharmacodynamics were assessed using the symbol digit modalities test (SDMT). The mean area under the curve (AUC) for midazolam was increased 771.9% by ketoconazole and 444.0% by nefazodone administration. However, there was no significant change in midazolam AUC as a result of fluoxetine (13.4% decrease) and a statistical trend for fluvoxamine (66.1% increase) administration. Pharmacodynamic data are consistent with pharmacokinetic data indicating that nefazodone and ketoconazole resulted in significant increases in midazolam-related cognition impairment. The significant impairment in subjects' cognitive function reflects the changes in midazolam clearance after treatment with ketoconazole and nefazodone. These results suggest that caution with the use of midazolam is warranted with potent CYP3A4 inhibitors.
Abstract: An antidepressant for use in the patient receiving concomitant drug treatment, over-the-counter medications, or herbal products should lack cytochrome P-450 (CYP) 3A4 inductive or inhibitory activity to provide the least likelihood of a drug-drug interaction. This study addresses the potential of 4 diverse antidepressants (venlafaxine, nefazodone, sertraline, and fluoxetine) to inhibit or induce CYP3A4. In a 4-way crossover design, 16 subjects received clinically relevant doses of venlafaxine, nefazodone, or sertraline for 8 days or fluoxetine for 11 days. Treatments were separated by a 7- to 14-day washout period and fluoxetine was always the last antidepressant taken. CYP3A4 activity was evaluated for each subject at baseline and following each antidepressant using the erythromycin breath test (EBT) and by the pharmacokinetics of alprazolam (ALPZ) after 2-mg dose of oral ALPZ. Compared to baseline, venlafaxine, sertraline, and fluoxetine caused no apparent inhibition or induction of erythromycin metabolism (P > 0.05). For nefazodone, a statistically significant inhibition was observed (P < 0.0005). Nefazodone was also the only antidepressant that caused a significant change in ALPZ disposition, decreasing its area under the concentration-versus-time curve (AUC; P < 0.01), and increasing its elimination half-life (16.4 vs. 12.3 hours; P < 0.05) compared with values at baseline. No significant differences were found in the pharmacokinetics of ALPZ with any of the other antidepressants tested. These results demonstrate in vivo that, unlike nefazodone, venlafaxine, sertraline, and fluoxetine do not possess significant metabolic inductive or inhibitory effects on CYP3A4.
Abstract: Ketoconazole is not known to be proarrhythmic without concomitant use of QT interval-prolonging drugs. We report a woman with coronary artery disease who developed a markedly prolonged QT interval and torsades de pointes (TdP) after taking ketoconazole for treatment of fungal infection. Her QT interval returned to normal upon withdrawal of ketoconazole. Genetic study did not find any mutation in her genes that encode cardiac IKr channel proteins. We postulate that by virtue of its direct blocking action on IKr, ketoconazole alone may prolong QT interval and induce TdP. This calls for attention when ketoconazole is administered to patients with risk factors for acquired long QT syndrome.
Abstract: OBJECTIVE: Our objective was to evaluate the effect of the CYP3A5 genotype on the pharmacokinetics and pharmacodynamics of alprazolam in healthy volunteers. METHODS: Nineteen healthy male volunteers were divided into 3 groups on the basis of the genetic polymorphism of CYP3A5. The groups comprised subjects with CYP3A5*1/*1 (n=5), CYP3A5*1/*3 (n=7), or CYP3A5*3/*3 (n=7). After a single oral 1-mg dose of alprazolam, plasma concentrations of alprazolam were measured up to 72 hours, together with assessment of psychomotor function by use of the Digit Symbol Substitution Test, according to CYP3A5 genotype. RESULTS: The area under the plasma concentration-time curve for alprazolam was significantly greater in subjects with CYP3A5*3/*3 (830.5+/-160.4 ng . h/mL [mean+/-SD]) than in those with CYP3A5*1/*1 (599.9+/-141.0 ng . h/mL) (P=.030). The oral clearance of alprazolam was also significantly different between the CYP3A5*1/*1 group (3.5+/-0.8 L/h) and CYP3A5*3/*3 group (2.5+/-0.5 L/h) (P=.036). Although a trend was noted for the area under the Digit Symbol Substitution Test score change-time curve (area under the effect curve) to be greater in subjects with CYP3A5*3/*3 (177.2+/-84.6) than in those with CYP3A5*1/*1 (107.5+/-44), the difference did not reach statistical significance (P=.148). CONCLUSIONS: The CYP3A5*3 genotype affects the disposition of alprazolam and thus influences the plasma levels of alprazolam.
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: OBJECTIVE: The antifungal drug ketoconazole (KTZ) is known as an inhibitor of, especially, the CYP3A subfamily, which catalyzes the metabolism of a large variety of drugs. Interactions between KTZ and CYP3A substrates have been reported both in vivo and in vitro. Most of them, however, involved the KTZ racemate. KTZ racemate and the separate enantiomers, 2R,4R; 2R,4S; 2S,4S, and 2S,4R, were evaluated for their selectivity in inhibiting alprazolam and quinine metabolism. METHODS: The inhibition of alprazolam and quinine metabolism was studied in an in vitro system of human liver microsomes (HLM), recombinant of CYP3A4 and CYP3A5. The concentrations of formed 3-hydroxyquinine and 4- and alpha-hydroxyalprazolam were measured by HPLC and LC-MS, respectively. RESULTS: Quinine 3-hydroxylation was catalyzed to a similar extent by CYP3A4 and CYP3A5. The formation rate of 4-hydroxyalprazolam was higher than that of alpha-hydroxyalprazolam for each HLM, CYP3A4 and CYP3A5. KTZ racemate and enantiomers showed differential inhibitory effects of quinine and alprazolam metabolism. Quinine metabolism catalyzed by HLM, CYP3A4 and CYP3A5 was potently inhibited by the trans-enantiomer KTZ 2S,4S, with IC(50) value of 0.16 microM for HLM, 0.04 microM for CYP3A4 and 0.11 microM for CYP3A5. The same enantiomer showed the lowest IC(50) values of 0.11 microM for HLM and 0.04 microM for CYP3A5 with respect to alprazoalm 4-hydroxylation and also the same pattern for alprazolamalpha-hydroxylation, 0.13 microM for HLM and 0.05 microM for CYP3A5. Alprazolam metabolism (both alpha- and 4- hydroxylations) catalyzed by CYP3A4 was inhibited potently by the cis-enantiomer KTZ 2S,4R, with IC(50) values of 0.03 microM. CONCLUSIONS: Alprazolam and quinine metabolism is catalyzed by both CYP3A4 and CYP3A5. The present study showed that different KTZ enantiomers inhibit CYP3A4 and CYP3A5 to different degrees, indicating that structural differences among the enantiomers would be related to their inhibitory potency on these two enzymes.
Abstract: OBJECTIVE: To investigate the effect of efavirenz on the ketoconazole pharmacokinetics in HIV-infected patients. METHODS: Twelve HIV-infected patients were assigned into a one-sequence, two-period pharmacokinetic interaction study. In phase one, the patients received 400 mg of ketoconazole as a single oral dose on day 1; in phase two, they received 600 mg of efavirenz once daily in combination with 150 mg of lamivudine and 30 or 40 mg of stavudine twice daily on days 2 to 16. On day 16, 400 mg of ketoconazole was added to the regimen as a single oral dose. Ketoconazole pharmacokinetics were studied on days 1 and 16. RESULTS: Pretreatment with efavirenz significantly increased the clearance of ketoconazole by 201%. C(max) and AUC(0-24) were significantly decreased by 44 and 72%, respectively. The T ((1/2)) was significantly shorter by 58%. CONCLUSION: Efavirenz has a strong inducing effect on the metabolism of ketoconazole.
Abstract: AIMS: To investigate the interaction between ketoconazole and darunavir (alone and in combination with low-dose ritonavir), in HIV-healthy volunteers. METHODS: Volunteers received darunavir 400 mg bid and darunavir 400 mg bid plus ketoconazole 200 mg bid, in two sessions (Panel 1), or darunavir/ritonavir 400/100 mg bid, ketoconazole 200 mg bid and darunavir/ritonavir 400/100 mg bid plus ketoconazole 200 mg bid, over three sessions (Panel 2). Treatments were administered with food for 6 days. Steady-state pharmacokinetics following the morning dose on day 7 were compared between treatments. Short-term safety and tolerability were assessed. RESULTS: Based on least square means ratios (90% confidence intervals), during darunavir and ketoconazole co-administration, darunavir area under the curve (AUC(12h)), maximum plasma concentration (C(max)) and minimum plasma concentration (C(min)) increased by 155% (80, 261), 78% (28, 147) and 179% (58, 393), respectively, compared with treatment with darunavir alone. Darunavir AUC(12h), C(max) and C(min) increased by 42% (23, 65), 21% (4, 40) and 73% (39, 114), respectively, during darunavir/ritonavir and ketoconazole co-administration, relative to darunavir/ritonavir treatment. Ketoconazole pharmacokinetics was unchanged by co-administration with darunavir alone. Ketoconazole AUC(12h), C(max) and C(min) increased by 212% (165, 268), 111% (81, 144) and 868% (544, 1355), respectively, during co-administration with darunavir/ritonavir compared with ketoconazole alone. CONCLUSIONS: The increase in darunavir exposure by ketoconazole was lower than that observed previously with ritonavir. A maximum ketoconazole dose of 200 mg day(-1) is recommended if used concomitantly with darunavir/ritonavir, with no dose adjustments for darunavir/ritonavir.
Abstract: The objective of this study was to measure the anticholinergic activity (AA) of medications commonly used by older adults. A radioreceptor assay was used to investigate the AA of 107 medications. Six clinically relevant concentrations were assessed for each medication. Rodent forebrain and striatum homogenate was used with tritiated quinuclidinyl benzilate. Drug-free serum was added to medication and atropine standard-curve samples. For medications that showed detectable AA, average steady-state peak plasma and serum concentrations (C(max)) in older adults were used to estimate relationships between in vitro dose and AA. All results are reported in pmol/mL of atropine equivalents. At typical doses administered to older adults, amitriptyline, atropine, clozapine, dicyclomine, doxepin, L-hyoscyamine, thioridazine, and tolterodine demonstrated AA exceeding 15 pmol/mL. Chlorpromazine, diphenhydramine, nortriptyline, olanzapine, oxybutynin, and paroxetine had AA values of 5 to 15 pmol/mL. Citalopram, escitalopram, fluoxetine, lithium, mirtazapine, quetiapine, ranitidine, and temazepam had values less than 5 pmol/mL. Amoxicillin, celecoxib, cephalexin, diazepam, digoxin, diphenoxylate, donepezil, duloxetine, fentanyl, furosemide, hydrocodone, lansoprazole, levofloxacin, metformin, phenytoin, propoxyphene, and topiramate demonstrated AA only at the highest concentrations tested (patients with above-average C(max) values, who receive higher doses, or are frail may show AA). The remainder of the medications investigated did not demonstrate any AA at the concentrations examined. Psychotropic medications were particularly likely to demonstrate AA. Each of the drug classifications investigated (e.g., antipsychotic, cardiovascular) had at least one medication that demonstrated AA at therapeutic doses. Clinicians can use this information when choosing between equally efficacious medications, as well as in assessing overall anticholinergic burden.
Abstract: OBJECTIVES: To examine the longitudinal relationship between cumulative exposure to anticholinergic medications and memory and executive function in older men. DESIGN: Prospective cohort study. SETTING: A Department of Veterans Affairs primary care clinic. PARTICIPANTS: Five hundred forty-four community-dwelling men aged 65 and older with diagnosed hypertension. MEASUREMENTS: The outcomes were measured using the Hopkins Verbal Recall Test (HVRT) for short-term memory and the instrumental activity of daily living (IADL) scale for executive function at baseline and during follow-up. Anticholinergic medication use was ascertained using participants' primary care visit records and quantified as total anticholinergic burden using a clinician-rated anticholinergic score. RESULTS: Cumulative exposure to anticholinergic medications over the preceding 12 months was associated with poorer performance on the HVRT and IADLs. On average, a 1-unit increase in the total anticholinergic burden per 3 months was associated with a 0.32-point (95% confidence interval (CI)= 0.05-0.58) and 0.10-point (95% CI=0.04-0.17) decrease in the HVRT and IADLs, respectively, independent of other potential risk factors for cognitive impairment, including age, education, cognitive and physical function, comorbidities, and severity of hypertension. The association was attenuated but remained statistically significant with memory (0.29, 95% CI=0.01-0.56) and executive function (0.08, 95% CI=0.02-0.15) after further adjustment for concomitant non-anticholinergic medications. CONCLUSION: Cumulative anticholinergic exposure across multiple medications over 1 year may negatively affect verbal memory and executive function in older men. Prescription of drugs with anticholinergic effects in older persons deserves continued attention to avoid deleterious adverse effects.
Abstract: BACKGROUND: Cognitive decline is common in Parkinson's disease (PD). Although some of the aetiological factors are known, it is not yet known whether drugs with anticholinergic activity (AA) contribute to this cognitive decline. Such knowledge would provide opportunities to prevent acceleration of cognitive decline in PD. OBJECTIVE: To study whether the use of agents with anticholinergic properties is an independent risk factor for cognitive decline in patients with PD. METHODS: A community-based cohort of patients with PD (n=235) were included and assessed at baseline. They were reassessed 4 and 8 years later. Cognition was assessed using the Mini-Mental State Examination (MMSE). A detailed assessment of the AA of all drugs prescribed was made, and AA was classified according to a standardised scale. Relationships between cognitive decline and AA load and duration of treatment were assessed using bivariate and multivariate statistical analyses. RESULTS: More than 40% used drugs with AA at baseline. During the 8-year follow-up, the cognitive decline was higher in those who had been taking AA drugs (median decline on MMSE 6.5 points) compared with those who had not taken such drugs (median decline 1 point; p=0.025). In linear regression analyses adjusting for age, baseline cognition and depression, significant associations with decline on MMSE were found for total AA load (standardised beta=0.229, p=0.04) as well as the duration of using AA drugs (standardised beta 0.231, p=0.032). CONCLUSION: Our findings suggest that there is an association between anticholinergic drug use and cognitive decline in PD. This may provide an important opportunity for clinicians to avoid increasing progression of cognitive decline by avoiding drugs with AA. Increased awareness by clinicians is required about the classes of drugs that have anticholinergic properties.
Abstract: BACKGROUND/AIMS: The nature and extent of adverse cognitive effects due to the prescription of anticholinergic drugs in older people with and without dementia is unclear. METHODS: We calculated the anticholinergic load (ACL) of medications taken by participants of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing, a cohort of 211 Alzheimer's disease (AD) patients, 133 mild cognitive impairment (MCI) patients and 768 healthy controls (HC) all aged over 60 years. The association between ACL and cognitive function was examined for each diagnostic group (HC, MCI, AD). RESULTS: A high ACL within the HC group was associated with significantly slower response speeds for the Stroop color and incongruent trials. No other significant relationships between ACL and cognition were noted. CONCLUSION: In this large cohort, prescribed anticholinergic drugs appeared to have modest effects upon psychomotor speed and executive function, but not on other areas of cognition in healthy older adults.
Abstract: This article reviews in vitro metabolic and in vivo pharmacokinetic drug-drug interactions of nine antifungal agents: six azoles (fluconazole, itraconazole, ketoconazole, miconazole, posaconazole, and voriconazole) and three echinocandins (anidulafungin, caspofungin, and micafungin). In in vitro interaction studies, itraconazole, ketoconazole, and miconazole were found to have higher inhibitory effects on cytochrome P450 (P450 or CYP) 3A4 and 3A5 activities than the other azoles or echinocandins did. Fluconazole, itraconazole, and voriconazole were relatively less potent inhibitors of CYP3A5 than of CYP3A4. The inhibitory effects of fluconazole, itraconazole, ketoconazole, and voriconazole against CYP3A4 and CYP3A5 seemed to be correlated with their dissociation constants for CYP51 (lanosterol 14α-demethylase) from Candida albicans. In in vivo pharmacokinetic studies, itraconazole was found to be a potent clinically important inhibitor of CYP3A4/5 substrates, and fluconazole and voriconazole increased the blood/plasma concentrations of not only CYP3A4/5 substrates but also CYP2C9 substrates. Miconazole was a potent inhibitor of all P450s investigated in vitro, although there are few detailed studies on the clinical significance of this except for CYP2C9. For the echinocandins, no marked inhibition of P450 activities, except for some inhibition of CYP3A4/5 activity, was observed in vitro. The blood/plasma concentrations of concomitant drugs were not markedly affected by coadministration of echinocandins in vivo, suggesting that echinocandins do not cause clinically significant interactions with drugs that are metabolized by P450s via the inhibition of metabolism. The differential effects of these antifungal agents on P450 activities must be considered when clinicians select antifungal agents for patients also receiving other drugs.
Abstract: BACKGROUND: Serotonin syndrome is a rare but serious complication of treatment with serotonergic agents. In its severe manifestations, death can ensue. Early recognition and aggressive management are crucial to mitigating the syndrome. Often the presentation can be subtle and easy to miss. CASE REPORTS: We present 2 cases of serotonin syndrome seen in the psychiatric consultation service of a busy academic hospital. Both patients had favorable outcomes because of early recognition and aggressive management. CONCLUSION: Physicians should carefully consider and rule out the clinical diagnosis of serotonin syndrome when presented with an agitated or confused patient who is taking serotonergic agents.
Abstract: 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: All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.
Abstract: The accurate estimation of "in vivo" inhibition constants () of inhibitors and fraction metabolized () of substrates is highly important for drug-drug interaction (DDI) prediction based on physiologically based pharmacokinetic (PBPK) models. We hypothesized that analysis of the pharmacokinetic alterations of substrate metabolites in addition to the parent drug would enable accurate estimation of in vivoandTwenty-four pharmacokinetic DDIs caused by P450 inhibition were analyzed with PBPK models using an emerging parameter estimation method, the cluster Newton method, which enables efficient estimation of a large number of parameters to describe the pharmacokinetics of parent and metabolized drugs. For each DDI, two analyses were conducted (with or without substrate metabolite data), and the parameter estimates were compared with each other. In 17 out of 24 cases, inclusion of substrate metabolite information in PBPK analysis improved the reliability of bothandImportantly, the estimatedfor the same inhibitor from different DDI studies was generally consistent, suggesting that the estimatedfrom one study can be reliably used for the prediction of untested DDI cases with different victim drugs. Furthermore, a large discrepancy was observed between the reported in vitroand the in vitro estimates for some inhibitors, and the current in vivoestimates might be used as reference values when optimizing in vitro-in vivo extrapolation strategies. These results demonstrated that better use of substrate metabolite information in PBPK analysis of clinical DDI data can improve reliability of top-down parameter estimation and prediction of untested DDIs.
Abstract: BACKGROUND: Anticholinergic drugs put elderly patients at a higher risk for falls, cognitive decline, and delirium as well as peripheral adverse reactions like dry mouth or constipation. Prescribers are often unaware of the drug-based anticholinergic burden (ACB) of their patients. This study aimed to develop an anticholinergic burden score for drugs licensed in Germany to be used by clinicians at prescribing level. METHODS: A systematic literature search in pubmed assessed previously published ACB tools. Quantitative grading scores were extracted, reduced to drugs available in Germany, and reevaluated by expert discussion. Drugs were scored as having no, weak, moderate, or strong anticholinergic effects. Further drugs were identified in clinical routine and included as well. RESULTS: The literature search identified 692 different drugs, with 548 drugs available in Germany. After exclusion of drugs due to no systemic effect or scoring of drug combinations (n = 67) and evaluation of 26 additional identified drugs in clinical routine, 504 drugs were scored. Of those, 356 drugs were categorised as having no, 104 drugs were scored as weak, 18 as moderate and 29 as having strong anticholinergic effects. CONCLUSIONS: The newly created ACB score for drugs authorized in Germany can be used in daily clinical practice to reduce potentially inappropriate medications for elderly patients. Further clinical studies investigating its effect on reducing anticholinergic side effects are necessary for validation.