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
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Si raccomanda il monitoraggio di fenitoina e quetiapina.
Concentrazioni di quetiapina significativamente ridotteMeccanismo: la quetiapina è metabolizzata principalmente dal CYP3A4. La fenitoina induce il CYP3A4, che può portare a una degradazione accelerata della quetiapina e quindi alle concentrazioni ridotte.
Effetto: l'accelerazione del metabolismo della quetiapina può portare a una riduzione delle concentrazioni. La clearance della quetiapina è aumentata di 5 volte in uno studio clinico con fenitoina. L'effetto neurolettico può quindi essere insufficiente. Occasionalmente sono state segnalate convulsioni come effetto indesiderato con quetiapina.
Misure: se l'associazione viene utilizzata come terapia cronica per più di 7-14 giorni, la dose originale di quetiapina deve essere aggiustata individualmente in base alla risposta clinica e alla tolleranza. Si consigliano controlli a specchio. Può essere necessario un aumento fino a 5 volte la dose originale di quetiapina. Se la fenitointterapia viene interrotta, la dose di quetiapina deve essere nuovamente ridotta entro 7-14 giorni. Deve essere effettuato un attento monitoraggio per quanto riguarda le indicazioni di un effetto inadeguato da un lato e, nel caso di aggiustamenti della dose rispetto alle indicazioni dei sintomi di tossicità (sedazione, tachicardia, ipotensione), dall'altro, dovrebbe essere effettuato.
|Fenitoina||1.1 [1.1,1.94] 1||1.1||n.a.|
I cambiamenti nell'esposizione menzionati si riferiscono ai cambiamenti nella curva concentrazione plasmatica-tempo [AUC]. Non abbiamo rilevato alcun cambiamento nell'esposizione alla cimetidina. Attualmente non è possibile stimare l'influenza di quetiapina e fenitoina. L'esposizione alla fenitoina aumenta al 110%, se combinato con cimetidina (110%). L'AUC è compresa tra 110% e 194% a seconda del
I parametri farmacocinetici della popolazione media sono utilizzati come punto di partenza per il calcolo delle singole variazioni di esposizione dovute alle interazioni.
La cimetidina ha una biodisponibilità orale media [ F ] del 65%, motivo per cui i livelli plasmatici massimi [Cmax] tendono a cambiare con un'interazione. L'emivita terminale [ t12 ] è piuttosto breve a 1.6333333 ore e i livelli plasmatici costanti [ Css ] vengono raggiunti rapidamente. Il legame proteico [ Pb ] è molto debole al 19% e il volume di distribuzione [ Vd ] è molto grande a 91 litri. Il metabolismo non avviene tramite i comuni citocromi e il trasporto attivo avviene in parte tramite BCRP e PGP.
La quetiapina ha una bassa biodisponibilità orale [ F ] del 9%, motivo per cui il livello plasmatico massimo [Cmax] tende a cambiare fortemente con un'interazione. L'emivita terminale [ t12 ] è piuttosto breve a 2.6 ore e i livelli plasmatici costanti [ Css ] vengono raggiunti rapidamente. Il legame proteico [ Pb ] è moderatamente forte al 83% e il volume di distribuzione [ Vd ] è di 55 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 tramite CYP2D6 e CYP3A4, tra gli altri e il trasporto attivo avviene in particolare tramite PGP.
La fenitoina ha un'elevata biodisponibilità orale [ F ] del 85%, motivo per cui i livelli plasmatici massimi [Cmax] tendono a cambiare poco durante un'interazione. L'emivita terminale [ t12 ] è di 13 ore e i livelli plasmatici costanti [ Css ] vengono raggiunti dopo circa 52 ore. Il legame proteico [ Pb ] è moderatamente forte al 90% e il volume di distribuzione [ Vd ] è di 47 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 tramite CYP2C19, CYP2C9 e CYP2E1, tra gli altri e il trasporto attivo avviene in particolare tramite PGP.
|Effetti serotoninergici a||0||Ø||Ø||Ø|
Valutazione: Secondo le nostre conoscenze, né la cimetidina, quetiapina né la fenitoina aumentano l'attività serotoninergica.
|Kiesel & Durán b||2||+||+||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 cimetidina e la quetiapina hanno 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. L'effetto anticolinergico della fenitoina non è rilevante.
Estensione di tempo QT
Valutazione: In combinazione, cimetidina e quetiapina possono potenzialmente innescare aritmie ventricolari di tipo torsione di punta. Non conosciamo alcun potenziale di prolungamento dell'intervallo QT per la fenitoina.
Effetti collaterali generali
|Effetti collaterali||∑ frequenza||cim||que||fen|
|Aumento di peso||15.5 %||n.a.||15.5↓||n.a.|
|Ipotensione ortostatica||7.0 %||n.a.||7.0↓||n.a.|
|Aumento dell'appetito||7.0 %||n.a.||7.0↓||n.a.|
Dispepsi (4.5%): quetiapina
Vomito (2%): fenitoina, quetiapina
Pancreatite: cimetidina, quetiapina
Ipertrofia gengivale: fenitoina
Ginecomastia (4%): cimetidina
Iperglicemia (2%): fenitoina, quetiapina
Dermatosi bollosa: fenitoina
Sindrome di Stevens Johnson: fenitoina, quetiapina
Necrolisi epidermica tossica: fenitoina
Leucopenia: fenitoina, quetiapina
Agranulocitosi: fenitoina, quetiapina
Compromissione della memoria: fenitoina
Disturbo da sogno: quetiapina
Sindrome neurolettica maligna: quetiapina
Discinesia tardiva: quetiapina
ALT aumentata: quetiapina
GGT elevato: quetiapina
Sindrome DRESS: fenitoina
Reazioni allergiche della pelle: quetiapina
Tromboembolia venosa: quetiapina
Sulla base delle vostre
Abstract: Phenytoin is a relatively insoluble weak acid, usually administered as the sodium salt. Bioavailability is dependent upon particle size and problems of generic inequivalence have therefore arisen, particularly in Scandinavia. The drug has a moderately large volume of distribution and is approximately 90% bound to plasma proteins. Clinically important displacement can be caused by bilirubin and several drugs, particularly sodium valproate, which is often combined with phenytoin. Displacement will lower the total serum concentration but will little affect the free drug concentration. The metabolism of phenytoin to the major metabolite, 5-(p-hydroxyphenyl)-5-(phenylhydantoin, is saturable, giving rise to a non linear dose-serum concentration relationship. Therefore, the dose range compatible with a therapeutic serum concentration is narrow within subjects, and monitoring serum concentrations is of particular value in dosage tailoring. In renal failure, the binding of phenytoin to plasma proteins is reduced and therefore a lower range of serum drug concentrations is compatible with therapeutic control. In liver disease, binding may also be impaired but delayed metabolism may occur in addition. During pregnancy the serum concentration may fall progressively as pregnancy advances, probably due to an increased rate of metabolism. Phenytoin readily crosses the placenta, and is metabolised rapidly by the neonate exposed in utero.
Abstract: 1. In a double-blind crossover study 10 healthy males received either placebo or omeprazole (40 mg day-1) for 9 days, a single dose of phenytoin (300 mg) being taken on the seventh day. 2. Omeprazole significantly increased the area under the curve (0 to 72 h) of phenytoin (mean +/- s.e. mean) from 121.6 +/- 14.0 to 151.4 +/- 13.6 micrograms ml-1 h) (P less than 0.01). 3. The peak concentration, and apparent elimination half-life of phenytoin also tended to be increased though not significantly. 4. The omeprazole-phenytoin interaction observed may be clinically important because of the low therapeutic index associated with phenytoin.
Abstract: Clearance of phenytoin after i.v. injection of 100 mg was studied in six patients before and after 2 weeks daily treatment with 450 mg rifampicin, and in 14 patients with tuberculosis receiving standard treatment with 450 mg rifampicin, 300 mg isoniazid, and 1200 mg ethambutol daily. Acetylator status was measured by urinary acetylated sulphadimidine. Clearance of phenytoin in patients receiving only rifampicin increased from 46.7 ml min-1 +/- 20.6 ml min-1 to 97.8 ml min-1 +/- 33.4 ml min-1 (P less than 0.01), while clearance in patients on three drugs increased from 47.1 +/- 23.4 ml min-1 to 81.3 ml min-1 +/- 41.6 ml min-1 (P less than 0.01). No significant differences were observed between the six fast acetylators and the eight slow acetylators. Phenytoin kinetics were unchanged after further 3 months of combined treatment. Rifampicin is a strong inducer of the elimination of phenytoin. Combined treatment with isoniazid has no counter-acting effect in either fast or slow acetylators.
Abstract: No Abstract available
Abstract: Recently, the use of astemizole and terfenadine, both non-sedating H1-antihistamines, caused considerable concern. Several case reports suggested an association of both drugs with an increased risk of torsades de pointes, a special form of ventricular tachycardia. The increased risk of both H1-antihistamines was associated with exposure to supratherapeutic doses; for terfenadine the risk was also associated with concomitant exposure to the cytochrome P-450 inhibitors ketoconazole, erythromycin and cimetidine. To predict the size of the population that runs the risk of developing this potentially fatal adverse reaction in the Netherlands, the prevalence of prescribing supratherapeutic doses and the concomitant exposure to terfenadine and cytochrome P-450 inhibitors was studied. Data were obtained from the PHARMO data base in 1990, a pharmacy-based record linkage system encompassing a catchment population of 300,000 individuals. The results of the study showed that the prescribing of supratherapeutic doses and the concomitant exposure to terfenadine and cytochrome P-450 inhibitors was low. Furthermore, the results of a sensitivity analysis showed that the risk of fatal torsades de pointes has to be as high as 1 in 10,000 to cause one death in the Netherlands in one year.
Abstract: Astemizole (Hismanal), an antihistamine agent, has been reported to be associated with ventricular arrhythmias. In this paper we present a case of QT prolongation and torsades de pointes (TdP) in a 77-year-old woman who had been taking astemizole (10 mg/day) for 6 months because of allergic skin disease. At the time of admission, the serum concentration of astemizole and its metabolites was markedly elevated at 15.85 ng/ml, approximately 3 times the normal level. The patient was also taking cimetidine, a known inhibitor of cytochrome P-450 enzymatic activity, and during her admission was diagnosed as having vasospastic angina. To the best of our knowledge, this is the first report of astemizole-induced QT prolongation and TdP in Japan.
Abstract: No Abstract available
Abstract: Quetiapine fumarate ('Seroquel') is a newly introduced atypical antipsychotic with demonstrated efficacy in the treatment of positive and negative symptoms of schizophrenia. It is extensively metabolized, predominantly by cytochrome P450 3A4. Therefore, concurrent administration of drugs that induce or inhibit this enzyme may affect quetiapine pharmacokinetics. This study demonstrated that the potent cytochrome P450 enzyme-inducer phenytoin did indeed have a marked effect on the metabolism of quetiapine, resulting in a 5-fold increase in clearance when administered concomitantly to patients with DSM-IV-diagnosed schizophrenia, schizoaffective disorder, or bipolar disorder. These results indicate that dosage adjustment of quetiapine may be necessary when the two drugs are given concurrently and that caution may be required when administering other drugs that inhibit or induce cytochromes, particularly P450 3A4.
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: Renal drug interactions can result from competitive inhibition between drugs that undergo extensive renal tubular secretion by transporters such as P-glycoprotein (P-gp). The purpose of this study was to evaluate the effect of itraconazole, a known P-gp inhibitor, on the renal tubular secretion of cimetidine in healthy volunteers who received intravenous cimetidine alone and following 3 days of oral itraconazole (400 mg/day) administration. Glomerular filtration rate (GFR) was measured continuously during each study visit using iothalamate clearance. Iothalamate, cimetidine, and itraconazole concentrations in plasma and urine were determined using high-performance liquid chromatography/ultraviolet (HPLC/UV) methods. Renal tubular secretion (CL(sec)) of cimetidine was calculated as the difference between renal clearance (CL(r)) and GFR (CL(ioth)) on days 1 and 5. Cimetidine pharmacokinetic estimates were obtained for total clearance (CL(T)), volume of distribution (Vd), elimination rate constant (K(el)), area under the plasma concentration-time curve (AUC(0-240 min)), and average plasma concentration (Cp(ave)) before and after itraconazole administration. Plasma itraconazole concentrations following oral dosing ranged from 0.41 to 0.92 microg/mL. The cimetidine AUC(0-240 min) increased by 25% (p < 0.01) following itraconazole administration. The GFR and Vd remained unchanged, but significant reductions in CL(T) (655 vs. 486 mL/min, p < 0.001) and CL(sec) (410 vs. 311 mL/min, p = 0.001) were observed. The increased systemic exposure of cimetidine during coadministration with itraconazole was likely due to inhibition of P-gp-mediated renal tubular secretion. Further evaluation of renal P-gp-modulating drugs such as itraconazole that may alter the renal excretion of coadministered drugs is warranted.
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: Antiepileptic and antipsychotic drugs are often prescribed together. Interactions between the drugs may affect both efficacy and toxicity. This is a review of human clinical data on the interactions between the antiepileptic drugs carbamazepine, valproic acid (sodium valproate), vigabatrin, lamotrigine, gabapentin, topiramate, tiagabine, oxcarbazepine, levetiracetam, pregabalin, felbamate, zonisamide, phenobarbital and phenytoin with the antipsychotic drugs risperidone, olanzapine, quetiapine, clozapine, amisulpride, sulpiride, ziprasidone, aripiprazole, haloperidol and chlorpromazine; the limited information on interactions between antiepileptic drugs and zuclopenthixol, periciazine, fluphenazine, flupenthixol and pimozide is also presented. Many of the interactions depend on the induction or inhibition of the cytochrome P450 isoenzymes, but other important mechanisms involve the uridine diphosphate glucuronosyltransferase isoenzymes and protein binding. There is some evidence for the following effects. Carbamazepine decreases the plasma concentrations of both risperidone and its active metabolite. It also decreases concentrations of olanzapine, clozapine, ziprasidone, haloperidol, zuclopenthixol, flupenthixol and probably chlorpromazine and fluphenazine. Quetiapine increases the ratio of carbamazepine epoxide to carbamazepine and this may lead to toxicity. The data on valproic acid are conflicting; it may either increase or decrease clozapine concentrations, and it appears to decrease aripiprazole concentrations. Chlorpromazine possibly increases valproic acid concentrations. Lamotrigine possibly increases clozapine concentrations. Phenobarbital decreases clozapine, haloperidol and chlorpromazine concentrations. Phenytoin decreases quetiapine, clozapine, haloperidol and possibly chlorpromazine concentrations. There are major gaps in the data. In many cases there are no published clinical data on interactions that would be predicted on theoretical grounds.
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: Posaconazole is an extended-spectrum triazole antifungal agent for the treatment and prophylaxis of invasive fungal infections. This randomized, open-label, parallel-group, multiple-dose study was conducted in healthy adult volunteers to assess the potential for a drug interaction between phenytoin and the posaconazole tablet formulation. METHODS: Subjects were randomly assigned for 10 days to one of the following treatments: posaconazole (200 mg once daily), phenytoin (200 mg once daily), or posaconazole (200 mg once daily) and phenytoin (200 mg once daily). Blood samples were collected on days 1 and 10 for pharmacokinetic evaluation of posaconazole and phenytoin concentrations. RESULTS: A total of 36 healthy men enrolled in the study. On day 1, the maximum plasma concentration (C(max)) and area under the concentration-time curve calculated from time 0-24 h post-dose (AUC(0-24)) were unchanged upon co-administration. At steady state (day 10), co-administration of posaconazole with phenytoin resulted in 44% (p = 0.012) and 52% (p = 0.007) decreases in posaconazole C(max) and AUC(0-24), respectively. These decreases in exposure corresponded with a 90% increase in steady-state clearance of orally administered posaconazole. Phenytoin C(max) and AUC(0-24) were not significantly altered upon co-administration of the two agents, 24% increase in C(max) (p = 0.196) and 25% increase in AUC(0-24) (p = 0.212) values, although inter-subject variability was observed within this group. CONCLUSION: Because co-administration of phenytoin and posaconazole significantly reduces posaconazole exposure and increases phenytoin levels in some subjects, concomitant use of these agents should be avoided unless the benefit outweighs the risk.
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: 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: 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: AIMS: To assess the role of MDR1 and gamma-aminobutyric acid receptor-gamma 2 sub unit (GABRG2) gene polymorphism in seizure susceptibility in generalized seizure (GS) and febrile seizure (FS) patients and to evaluate MDR1 C3435T gene polymorphism's role in absorption of the anti-epileptic drug, phenytoin (PHT) in a cohort of patients. METHODS: One hundred twenty-seven cases of seizure (86 GS and 41 FS) patients were analyzed for MDR1 C3435T and GABRG2 C588T gene polymorphisms using restriction fragment length polymorphism-polymerase chain reaction. Serum PHT levels were analyzed. RESULTS: The T allele of MDR1 C3435T and GABRG2 C588T gene polymorphism was higher in GS in the Indian population compared with controls. From the data in GS, CT and TT genotype carriers of the MDR1 gene and TT genotype carriers of the GABRG2 gene had more recurrent seizures compared with others. MDR1 T allele carriers in the seizure reoccurrence (SR) group of GS and FS were high compared with the well-controlled seizure group (with no seizures after treatment). TT genotype carriers in SR group were high in FS (with regard to MDR1 gene polymorphism) and GS (with regard to GABRG2 gene polymorphism) compared with a well-controlled seizure group. MDR1 C3435T gene polymorphism affects serum PHT levels (p<0.015). Association of dose PHT ratio and genotype groups of MDR1 C3435T gene polymorphism showed a significant association (p<0.05). MDR1*CC genotype was more common in cases with low serum PHT levels.In addition, it is evident that CT and TT genotype carriers have a high percentage of SR with elevated serum PHT levels. CONCLUSIONS: Our results show that the MDR1 3435T and GABRG2 588T alleles play a role in seizure occurrence. Moreover, the MDR1 3435T allele also affects PHT absorption. We suggest MDR1 C3435T and GABRG2 C588T genotyping would be of value in order to lower the risk of concentration-dependent drug toxicity and for better patient management.
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: 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: P-glycoprotein (P-gp), an ATP-dependant efflux pump transports a wide range of substrates across cellular membranes. Earlier studies have identified drug efflux due to the over-expression of P-gp as one of the causes for the resistance of phenytoin, an anti-epileptic drug (AED). While no clear evidence exists on the specific characteristics of phenytoin association with the human P-gp, this study employed structure-based computational approaches to identify its binding site and the underlying interactions. The identified site was validated with that of rhodamine, a widely accepted reference and an experimental probe. Further, an in silico proof-of-concept for phenytoin interactions and its decreased binding affinity with the closed-state of human P-gp model was provided in comparison with other AEDs. This is the first report to provide insights into the phenytoin binding site and possibly better explain its efflux by P-gp.
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: AIM: Conducting PK studies in pregnant women is challenging. Therefore, we asked if a physiologically-based pharmacokinetic (PBPK) model could be used to predict the disposition in pregnant women of drugs cleared by multiple CYP enzymes. METHODS: We expanded and verified our previously published pregnancy PBPK model by incorporating hepatic CYP2B6 induction (based on in vitro data), CYP2C9 induction (based on phenytoin PK) and CYP2C19 suppression (based on proguanil PK), into the model. This model accounted for gestational age-dependent changes in maternal physiology and hepatic CYP3A, CYP1A2 and CYP2D6 activity. For verification, the pregnancy-related changes in the disposition of methadone (cleared by CYP2B6, 3A and 2C19) and glyburide (cleared by CYP3A, 2C9 and 2C19) were predicted. RESULTS: Predicted mean post-partum to second trimester (PP : T2 ) ratios of methadone AUC, Cmax and Cmin were 1.9, 1.7 and 2.0, vs. observed values 2.0, 2.0 and 2.6, respectively. Predicted mean post-partum to third trimester (PP : T3 ) ratios of methadone AUC, Cmax and Cmin were 2.1, 2.0 and 2.4, vs. observed values 1.7, 1.7 and 1.8, respectively. Predicted PP : T3 ratios of glyburide AUC, Cmax and Cmin were 2.6, 2.2 and 7.0 vs. observed values 2.1, 2.2 and 3.2, respectively. CONCLUSIONS: Our PBPK model integrating prior physiological knowledge, in vitro and in vivo data, allowed successful prediction of methadone and glyburide disposition during pregnancy. We propose this expanded PBPK model can be used to evaluate different dosing scenarios, during pregnancy, of drugs cleared by single or multiple CYP enzymes.
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: 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: 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.