Allongement du temps QT
Événements indésirables médicamenteux
|Créatine kinase élevée|
Variantes ✨Pour une évaluation intensive des variantes par ordinateur, veuillez choisir l'abonnement standard payant.
Explications concernant les substances pour les patients
Nous n'avons pas de mise en garde supplémentaire concernant l'association de itraconazole, clarithromycine et de cobimétinib. Veuillez également consulter les informations pertinentes des spécialistes.
Les changements d'exposition rapportés correspondent aux changements de la courbe concentration-temps plasmatique [ AUC ]. L'exposition à la cobimétinib augmente à 692%, lorsqu'il est associé à la itraconazole (551%) et à la clarithromycine (569%). Cela peut entraîner un taux d'incidence plus élevé des effets secondaires. L'exposition à la itraconazole augmente à 367%, lorsqu'il est associé à la clarithromycine (367%) et à la cobimétinib (100%). Cela peut entraîner un taux d'incidence plus élevé des effets secondaires. L'exposition à la clarithromycine augmente à 171%, lorsqu'il est associé à la itraconazole (171%) et à la cobimétinib (100%). Cela peut entraîner un taux d'incidence plus élevé des effets secondaires.
Les paramètres pharmacocinétiques de la population moyenne sont utilisés comme point de départ pour calculer les changements individuels d'exposition dus aux interactions.
La itraconazole a une biodisponibilité orale moyenne [ F ] de 55%, c'est pourquoi les concentrations plasmatiques maximales [Cmax] ont tendance à changer avec une interaction. La demi-vie terminale [ t12 ] est de 21 heures et des taux plasmatiques constants [ Css ] sont atteints après environ 84 heures. La liaison aux protéines [ Pb ] est très forte à 99.8% et le volume de distribution [ Vd ] est très grand à 796 litres, c'est pourquoi, avec un taux d'extraction hépatique moyen de 0.44, le débit sanguin hépatique [Q] et une modification de la liaison aux protéines [Pb] sont pertinents. Le métabolisme se fait principalement via CYP3A4 et le transport actif s'effectue notamment via PGP.
La clarithromycine a une biodisponibilité orale moyenne [ F ] de 53%, c'est pourquoi les concentrations plasmatiques maximales [Cmax] ont tendance à changer avec une interaction. La demi-vie terminale [ t12 ] est assez courte (4.6 heures) et des taux plasmatiques constants [ Css ] sont rapidement atteints. La liaison aux protéines [ Pb ] est plutôt faible à 70% et le volume de distribution [ Vd ] est très grand à 176 litres. Étant donné que la substance a un faible taux d'extraction hépatique de 0.13, le déplacement de la liaison aux protéines [Pb] dans le contexte d'une interaction peut entraîner une augmentation de l'exposition. Environ 27.5% d'une dose administrée sont excrétés sous forme inchangée par les reins et cette proportion est rarement modifiée par les interactions. Le métabolisme se fait principalement via CYP3A4 et le transport actif s'effectue notamment via PGP.
La cobimétinib a une biodisponibilité orale moyenne [ F ] de 46%, c'est pourquoi les concentrations plasmatiques maximales [Cmax] ont tendance à changer avec une interaction. La demi-vie terminale [ t12 ] est assez longue (jusqu'à 44 heures) et des taux plasmatiques constants [ Css ] ne sont atteints qu'après plus de 176 heures. La liaison aux protéines [ Pb ] est modérément forte à 95%. Étant donné que la substance a un faible taux d'extraction hépatique de 0.07, le déplacement de la liaison aux protéines [Pb] dans le contexte d'une interaction peut entraîner une augmentation de l'exposition. Le métabolisme se fait principalement via CYP3A4 et le transport actif s'effectue en partie via PGP et UGT2B7.
|Effets sérotoninergiques a||0||Ø||Ø||Ø|
Note: À notre connaissance, ni la itraconazole, clarithromycine ni la cobimétinib n'augmentent l'activité sérotoninergique.
|Kiesel & Durán b||0||Ø||Ø||Ø|
Notation: À notre connaissance, ni la itraconazole, clarithromycine ni la cobimétinib n'augmentent l'activité anticholinergique.
Allongement du temps QT
Note: En association, la itraconazole, clarithromycine et la cobimétinib peuvent potentiellement déclencher des arythmies ventriculaires de type torsades de pointes.
Effets indésirables généraux
|Effets secondaires||∑ fréquence||itr||cla||cob|
|Créatine kinase élevée||79.0 %||n.a.||n.a.||79.0↑|
|AST élevé||73.0 %||n.a.||n.a.||73.0↑|
|Phosphatase alcaline élevée||71.0 %||n.a.||n.a.||71.0↑|
|ALT élevé||68.0 %||n.a.||n.a.||68.0↑|
|La diarrhée||63.3 %||2.9↑||5.5||60.0↑|
|La nausée||53.6 %||7.0↑||15.5||41.0↑|
Démangeaison de la peau (21%): cobimétinib, itraconazole
Carcinome basocellulaire de la peau (4.5%): cobimétinib
Prurit (4%): itraconazole
Mélanome malin: cobimétinib
Carcinome squameux: cobimétinib
Syndrome de Stevens-Johnson: clarithromycine
Nécrolyse épidermique toxique: clarithromycine
Vomissements (17.4%): clarithromycine, itraconazole
Trouble du goût (13.5%): clarithromycine
Douleur abdominale (7.2%): clarithromycine, itraconazole
Dyspepsie (4%): clarithromycine
Hémorragie gastro-intestinale (3.6%): cobimétinib
Diarrhée à Clostridium difficile: clarithromycine
Mal de crâne (14.6%): clarithromycine, itraconazole
Vertiges (2.6%): itraconazole
Hémorragie intracrânienne: cobimétinib
Rhinopharyngite (9%): itraconazole
Infection respiratoire supérieure (8%): itraconazole
Sinusite (4.5%): itraconazole
Œdème pulmonaire: itraconazole
Œdème périphérique (4%): itraconazole
Hypertension (3%): itraconazole
Insuffisance cardiaque: itraconazole
Fièvre (2.5%): itraconazole
Fatigue (2.3%): itraconazole
Hématurie (2.4%): cobimétinib
Hémorragie (1.2%): cobimétinib
Hépatotoxicité: cobimétinib, clarithromycine, itraconazole
Hépatite cholestatique: clarithromycine
Pancréatite: clarithromycine, itraconazole
Rhabdomyolyse: cobimétinib, clarithromycine
Réaction anaphylactique: clarithromycine
Réaction d'hypersensibilité: itraconazole
Perte auditive: itraconazole
Sur la base de vos réponses et des informations scientifiques, nous évaluons le risque individuel d'effets secondaires indésirables. Ces recommandations sont destinées à conseiller les professionnels et ne se substituent pas à la consultation d'un médecin. Dans la version d'essai (alpha), le risque de toutes les substances n'a pas encore été évalué de manière concluante.
Abstract: No Abstract available
Abstract: Erythromycin, clarithromycin, and azithromycin are clinically effective for the treatment of common respiratory and skin/skin-structure infections. Erythromycin and azithromycin are also effective for treatment of nongonococcal urethritis and cervicitis due to Chlamydia trachomatis. Compared with erythromycin, clarithromycin and azithromycin offer improved tolerability. Clarithromycin, however, is more similar to erythromycin in pharmacokinetic measures such as half-life, tissue distribution, and drug interactions. Misunderstandings about differences among the macrolides (erythromycin and clarithromycin) and the azalide (azithromycin) in terms of pharmacokinetics and pharmacodynamics, spectrum of activity, safety, and cost are common. The uptake and release of these compounds by white blood cells and fibroblasts account for differences in tissue half-life, volume of distribution, intracellular:extracellular ratio, and in vivo potency. Although microbiologic studies reveal that gram-positive pathogens are equally susceptible to these agents, significantly more isolates of Haemophilus influenzae are susceptible to azithromycin than to erythromycin or clarithromycin. Concentrations achieved at the infection site and duration above the minimum inhibitory concentration are as important as in vitro activity in determining in vivo activity against bacterial pathogens. Analysis of safety data indicates differences among these agents in drug interactions and use in pregnancy. Analysis of safety data reveals pharmacokinetic drug interactions for erythromycin and clarithromycin with theophylline, terfenadine, and carbamazepine that are not found with azithromycin. Both erythromycin and azithromycin are pregnancy category B drugs; clarithromycin is a category C drug. The numerous differences in pharmacokinetics, microbiology, safety, and costs among erythromycin, clarithromycin, and azithromycin can be used in the judicious selection of treatment for indicated infections.
Abstract: To investigate whether grapefruit juice inhibits the metabolism of clarithromycin, 12 healthy subjects were given water or grapefruit juice before and after a clarithromycin dose of 500 mg in a randomized crossover study. Administration of grapefruit juice increased the time to peak concentration of both clarithromycin (82 +/- 35 versus 148 +/- 83 min; P = 0.02) and 14-hydroxyclarithromycin (84 +/- 38 min versus 173 +/- 85; P = 0.01) but did not affect other pharmacokinetic parameters.
Abstract: No Abstract available
Abstract: Clarithromycin is a macrolide antibacterial that differs in chemical structure from erythromycin by the methylation of the hydroxyl group at position 6 on the lactone ring. The pharmacokinetic advantages that clarithromycin has over erythromycin include increased oral bioavailability (52 to 55%), increased plasma concentrations (mean maximum concentrations ranged from 1.01 to 1.52 mg/L and 2.41 to 2.85 mg/L after multiple 250 and 500 mg doses, respectively), and a longer elimination half-life (3.3 to 4.9 hours) to allow twice daily administration. In addition, clarithromycin has extensive diffusion into saliva, sputum, lung tissue, epithelial lining fluid, alveolar macrophages, neutrophils, tonsils, nasal mucosa and middle ear fluid. Clarithromycin is primarily metabolised by cytochrome P450 (CYP) 3A isozymes and has an active metabolite, 14-hydroxyclarithromycin. The reported mean values of total body clearance and renal clearance in adults have ranged from 29.2 to 58.1 L/h and 6.7 to 12.8 L/h, respectively. In patients with severe renal impairment, increased plasma concentrations and a prolonged elimination half-life for clarithromycin and its metabolite have been reported. A dosage adjustment for clarithromycin should be considered in patients with a creatinine clearance < 1.8 L/h. The recommended goal for dosage regimens of clarithromycin is to ensure that the time that unbound drug concentrations in the blood remains above the minimum inhibitory concentration is at least 40 to 60% of the dosage interval. However, the concentrations and in vitro activity of 14-hydroxyclarithromycin must be considered for pathogens such as Haemophilus influenzae. In addition, clarithromycin achieves significantly higher drug concentrations in the epithelial lining fluid and alveolar macrophages, the potential sites of extracellular and intracellular respiratory tract pathogens, respectively. Further studies are needed to determine the importance of these concentrations of clarithromycin at the site of infection. Clarithromycin can increase the steady-state concentrations of drugs that are primarily depend upon CYP3A metabolism (e.g., astemidole, cisapride, pimozide, midazolam and triazolam). This can be clinically important for drugs that have a narrow therapeutic index, such as carbamazepine, cyclosporin, digoxin, theophylline and warfarin. Potent inhibitors of CYP3A (e.g., omeprazole and ritonavir) may also alter the metabolism of clarithromycin and its metabolites. Rifampicin (rifampin) and rifabutin are potent enzyme inducers and several small studies have suggested that these agents may significantly decrease serum clarithromycin concentrations. Overall, the pharmacokinetic and pharmacodynamic studies suggest that fewer serious drug interactions occur with clarithromycin compared with older macrolides such as erythromycin and troleandomycin.
Abstract: Two cases of QT prolongation and torsades de pointes (TdP) are presented. The patients had been taking clarithromycin (400 mg/day) for respiratory disease. Although erythromycin is reportedly associated with TdP, this is the first report of clarithromycin associated with TdP in the absence of other drugs already known to produce QT prolongation.
Abstract: Itraconazole (ITZ) is a potent inhibitor of CYP3A in vivo. However, unbound plasma concentrations of ITZ are much lower than its reported in vitro Ki, and no clinically significant interactions would be expected based on a reversible mechanism of inhibition. The purpose of this study was to evaluate the reasons for the in vitro-in vivo discrepancy. The metabolism of ITZ by CYP3A4 was studied. Three metabolites were detected: hydroxy-itraconazole (OH-ITZ), a known in vivo metabolite of ITZ, and two new metabolites: keto-itraconazole (keto-ITZ) and N-desalkyl-itraconazole (ND-ITZ). OHITZ and keto-ITZ were also substrates of CYP3A4. Using a substrate depletion kinetic approach for parameter determination, ITZ exhibited an unbound K(m) of 3.9 nM and an intrinsic clearance (CLint) of 69.3 ml.min(-1).nmol CYP3A4(-1). The respective unbound Km values for OH-ITZ and keto-ITZ were 27 nM and 1.4 nM and the CLint values were 19.8 and 62.5 ml.min(-1).nmol CYP3A4(-1). Inhibition of CYP3A4 by ITZ, OH-ITZ, keto-ITZ, and ND-ITZ was evaluated using hydroxylation of midazolam as a probe reaction. Both ITZ and OH-ITZ were competitive inhibitors of CYP3A4, with unbound Ki (1.3 nM for ITZ and 14.4 nM for OH-ITZ) close to their respective Km. ITZ, OH-ITZ, keto-ITZ and ND-ITZ exhibited unbound IC50 values of 6.1 nM, 4.6 nM, 7.0 nM, and 0.4 nM, respectively, when coincubated with human liver microsomes and midazolam (substrate concentration < Km). These findings demonstrate that ITZ metabolites are as potent as or more potent CYP3A4 inhibitors than ITZ itself, and thus may contribute to the inhibition of CYP3A4 observed in vivo after ITZ dosing.
Abstract: Itraconazole (ITZ) is metabolized in vitro to three inhibitory metabolites: hydroxy-itraconazole (OH-ITZ), keto-itraconazole (keto-ITZ), and N-desalkyl-itraconazole (ND-ITZ). The goal of this study was to determine the contribution of these metabolites to drug-drug interactions caused by ITZ. Six healthy volunteers received 100 mg ITZ orally for 7 days, and pharmacokinetic analysis was conducted at days 1 and 7 of the study. The extent of CYP3A4 inhibition by ITZ and its metabolites was predicted using this data. ITZ, OH-ITZ, keto-ITZ, and ND-ITZ were detected in plasma samples of all volunteers. A 3.9-fold decrease in the hepatic intrinsic clearance of a CYP3A4 substrate was predicted using the average unbound steady-state concentrations (C(ss,ave,u)) and liver microsomal inhibition constants for ITZ, OH-ITZ, keto-ITZ, and ND-ITZ. Accounting for circulating metabolites of ITZ significantly improved the in vitro to in vivo extrapolation of CYP3A4 inhibition compared to a consideration of ITZ exposure alone.
Abstract: PURPOSE: The objective is to confirm if the prediction of the drug-drug interaction using a physiologically based pharmacokinetic (PBPK) model is more accurate. In vivo Ki values were estimated using PBPK model to confirm whether in vitro Ki values are suitable. METHOD: The plasma concentration-time profiles for the substrate with coadministration of an inhibitor were collected from the literature and were fitted to the PBPK model to estimate the in vivo Ki values. The AUC ratios predicted by the PBPK model using in vivo Ki values were compared with those by the conventional method assuming constant inhibitor concentration. RESULTS: The in vivo Ki values of 11 inhibitors were estimated. When the in vivo Ki values became relatively lower, the in vitro Ki values were overestimated. This discrepancy between in vitro and in vivo Ki values became larger with an increase in lipophilicity. The prediction from the PBPK model involving the time profile of the inhibitor concentration was more accurate than the prediction by the conventional methods. CONCLUSION: A discrepancy between the in vivo and in vitro Ki values was observed. The prediction using in vivo Ki values and the PBPK model was more accurate than the conventional methods.
Abstract: The involvement of intestinal permeability in the oral absorption of clarithromycin (CAM), a macrolide antibiotic, and telithromycin (TEL), a ketolide antibiotic, in the presence of efflux transporters was examined. In order independently to examine the intestinal and hepatic availability, CAM and TEL (10 mg/kg) were administered orally, intraportally and intravenously to rats. The intestinal and hepatic availability was calculated from the area under the plasma concentration-time curve (AUC) after administration of CAM and TEL via different routes. The intestinal availabilities of CAM and TEL were lower than their hepatic availabilities. The intestinal availability after oral administration of CAM and TEL increased by 1.3- and 1.6-fold, respectively, after concomitant oral administration of verapamil as a P-glycoprotein (P-gp) inhibitor. Further, an in vitro transport experiment was performed using Caco-2 cell monolayers as a model of intestinal epithelial cells. The apical-to-basolateral transport of CAM and TEL through the Caco-2 cell monolayers was lower than their basolateral-to-apical transport. Verapamil and bromosulfophthalein as a multidrug resistance-associated proteins (MRPs) inhibitor significantly increased the apical-to-basolateral transport of CAM and TEL. Thus, the results suggest that oral absorption of CAM and TEL is dependent on intestinal permeability that may be limited by P-gp and MRPs on the intestinal epithelial cells.
Abstract: The pharmacokinetics, metabolism, and excretion of cobimetinib, a MEK inhibitor, were characterized in healthy male subjects (n = 6) following a single 20 mg (200 μCi) oral dose. Unchanged cobimetinib and M16 (glycine conjugate of hydrolyzed cobimetinib) were the major circulating species, accounting for 20.5% and 18.3% of the drug-related material in plasma up to 48 hours postdose, respectively. Other circulating metabolites were minor, accounting for less than 10% of drug-related material in plasma. The total recovery of the administered radioactivity was 94.3% (±1.6%, S.D.) with 76.5% (±2.3%) in feces and 17.8% (±2.5%) in urine. Metabolite profiling indicated that cobimetinib had been extensively metabolized with only 1.6% and 6.6% of the dose remaining as unchanged drug in urine and feces, respectively. In vitro phenotyping experiments indicated that CYP3A4 was predominantly responsible for metabolizing cobimetinib. From this study, we concluded that cobimetinib had been well absorbed (fraction absorbed, Fa = 0.88). Given this good absorption and the previously determined low hepatic clearance, the systemic exposures were lower than expected (bioavailability, F = 0.28). We hypothesized that intestinal metabolism had strongly attenuated the oral bioavailability of cobimetinib. Supporting this hypothesis, the fraction escaping gut wall elimination (Fg) was estimated to be 0.37 based on F and Fa from this study and the fraction escaping hepatic elimination (Fh) from the absolute bioavailability study (F = Fa × Fh × Fg). Physiologically based pharmacokinetics modeling also showed that intestinal clearance had to be included to adequately describe the oral profile. These collective data suggested that cobimetinib was well absorbed following oral administration and extensively metabolized with intestinal first-pass metabolism contributing to its disposition.
Abstract: BACKGROUND AND OBJECTIVES: Cobimetinib is eliminated mainly through cytochrome P450 (CYP) 3A4-mediated hepatic metabolism in humans. A clinical drug-drug interaction (DDI) study with the potent CYP3A4 inhibitor itraconazole resulted in an approximately sevenfold increase in cobimetinib exposure. The DDI risk for cobimetinib with other CYP3A4 inhibitors and inducers needs to be assessed in order to provide dosing instructions. METHODS: A physiologically based pharmacokinetic (PBPK) model was developed for cobimetinib using in vitro data. It was then optimized and verified using clinical pharmacokinetic data and itraconazole-cobimetinib DDI data. The contribution of CYP3A4 to the clearance of cobimetinib in humans was confirmed using sensitivity analysis in a retrospective simulation of itraconazole-cobimetinib DDI data. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors and inducers on cobimetinib pharmacokinetics. RESULTS: The PBPK model described cobimetinib pharmacokinetic profiles after both intravenous and oral administration of cobimetinib well and accurately simulated the itraconazole-cobimetinib DDI. Sensitivity analysis suggested that CYP3A4 contributes ~78 % of the total clearance of cobimetinib. The PBPK model predicted no change in cobimetinib exposure (area under the plasma concentration-time curve, AUC) with the weak CYP3A inhibitor fluvoxamine and a three to fourfold increase with the moderate CYP3A inhibitors, erythromycin and diltiazem. Similarly, cobimetinib exposure in the presence of strong (rifampicin) and moderate (efavirenz) CYP3A inducers was predicted to decrease by 83 and 72 %, respectively. CONCLUSION: This study demonstrates the value of using PBPK simulation to assess the clinical DDI risk inorder to provide dosing instructions with other CYP3A4 perpetrators.
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: 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.