Intervallo QT lungo
Reazione avversa da farmaco (ADR)
Varianti ✨Per l'analisi computazionale dettagliata delle varianti, si prega di selezionare l'abbonamento standard a pagamento.
Informazioni dei farmaci per i pazienti
Non abbiamo ulteriori avvertenze per la co-somministrazione di abarelix e lansoprazolo. Si prega di consultare le informazioni specialistiche pertinenti.
|Lansoprazolo||1 [0.69,4.04] 1||1|
I cambiamenti riportati in seguito all'esposizione corrispondono ai cambiamenti nell'area sottesa alla curva concentrazione plasmatica-tempo [ AUC ]. Non ci aspettiamo nessun cambiamento nell'esposizione alla abarelix, quando è co-somministrata con la lansoprazolo (100%). Non ci aspettiamo nessun cambiamento nell'esposizione alla lansoprazolo, quando è co-somministrata con la abarelix (100%). L' AUC è compreso tra lo 69% e il 404% in base al
I parametri farmacocinetici della popolazione media sono utilizzati come punto di partenza per calcolare i cambiamenti del singolo individuo esposto alle interazioni farmacologiche
La biodisponibilità della abarelix non è nota. L'emivita [ t12 ] del farmaco è piuttosto lunga in 316.8 ore e concentrazioni plasmatiche allo stato stazionario [Css] si raggiungono dopo più di 1267.2 ore. Il legame proteico [ Pb ] è forte al 97.5%. I processi metabolici che avvengono tramite il sistema enzimatico dei citocromi sono ancora in fase di studio..
La lansoprazolo ha una significativa biodisponibilità [ F ] orale pari al 80%, perciò attraverso un'interazione farmacologica la concentrazione plasmatica massima [Cmax] tende a cambiare di poco. L'emivita [ t12 ] del farmaco è piuttosto breve in 0.9 ore e lo stato stazionario [Css] si raggiunge molto velocemente. Il legame proteico [ Pb ] è forte al 97% e il volume di distribuzione [ Vd ] è piccolo in 12 litri. Tra l'altro, il metabolismo avviene rispettivamente attraverso gli enzimi CYP2C19 e CYP3A4. e il trasporto attivo avviene in particolare attraverso i trasportatori PGP e TRA8X8.
|Effetti serotoninergici a||0||Ø||Ø|
Valutazione: Sulla base dei dati a nostra disposizione, né la abarelix né la lansoprazolo potenziano l'attività serotoninergica.
|Kiesel & Durán b||0||Ø||Ø|
Valutazione: Sulla base dei dati a nostra disposizione, la abarelix non causa un aumento dell'attività anticolinergica. L'effetto anticolingerico della lansoprazolo non è rilevante.
Intervallo QT lungo
Valutazione: La co-somministrazione di abarelix e lansoprazolo potrebbe causare tachicardia ventricolare a torsione di punta.
Effetti collaterali generali
|Effetti collaterali||∑ frequenza||aba||lan|
|Dolore addominale||5.0 %||n.a.||5.0|
|Mal di testa||1.0 %||n.a.||+|
|Lupus eritematoso cutaneo||0.0 %||n.a.||0.0|
|Sindrome di Stevens Johnson||0.0 %||n.a.||0.0|
|Necrolisi epidermica tossica||0.0 %||n.a.||0.0|
|Diarrea da Clostridium difficile||0.0 %||n.a.||0.0|
Reazione di ipersensibilità: lansoprazolo
Nefrite tubulointerstiziale: lansoprazolo
Abbiamo valutato il rischio individuale di effetti indesiderati in base alle risposte fornite ed alle informazioni scientifiche disponibili. Le informazioni contenute nel sito hanno esclusivamente scopo informativo e non sostituiscono il parere del medico. Si accomanda pertanto di chiedere sempre il parere del proprio medico curante e/o di specialisti riguardo qualsiasi indicazione riportata. Nella versione alpha test, il rischio di tutti i farmaci non è stato ancora completamente valutato.
Abstract: OBJECTIVE: In a crossover study 12 healthy volunteers received lansoprazole 15 mg or 30 mg orally, or 15 mg intravenously in randomized order as a single dose. Blood samples were taken and plasma levels of lansoprazole were determined using an HPLC method. The volunteers were phenotyped for the debrisoquine/sparteine and mephenytoin polymorphisms. RESULTS: The total clearance was 517 ml.min-1, and the absolute bioavailability was 91% for the 30-mg and 81% for the 15-mg enteric-coated formulation. The elimination half-life was about 1 h. No correlation of the plasma levels to the sparteine metabolic ratio was found, and no correlation to the mephenytoin type could be established, since all volunteers of the mephenytoin type were extensive metabolizers. Although considerable variation, inter- and intraindividually, was observed, the increase in Cmax and AUC did not deviate from dose proportionality. The present galenic formulation ensures a high bioavailability after a single dose.
Abstract: Lansoprazole is a substrate of CYP2C19 and CYP3A4. The aim of this study was to compare the inhibitory effects of fluvoxamine, an inhibitor of CYP2C19, on the metabolism of lansoprazole between CYP2C19 genotypes. Eighteen volunteers--of whom 6 were homozygous extensive metabolizers (EMs), 6 were heterozygous EMs, and 6 were poor metabolizers (PMs) for CYP2C19--received three 6-day courses of either daily 50 mg fluvoxamine or placebo in a randomized fashion with a single oral 60-mg dose of lansoprazole on day 6 in all cases. Plasma concentrations of lansoprazole and its metabolites, 5-hydroxylansoprazole and lansoprazole sulfone, were monitored up to 24 hours after the dosing. During placebo administration, there was a significant difference in the area under the plasma concentration-time curve from time 0 to infinity (AUC(0-infinity)) of lansoprazole between CYP2C19 genotypes. Fluvoxamine treatment increased AUC(0-infinity) of lansoprazole by 3.8-fold (P < .01) in homozygous EMs and by 2.5-fold (P < .05) in heterozygous EMs, whereas no difference in any pharmacokinetic parameters was found in PMs. There was a significant difference in the fluvoxamine-mediated percentage increase in the AUC(0-infinity) of lansoprazole between CYP2C19 genotypes. The present study indicates that there are significant drug interactions between lansoprazole and fluvoxamine in EMs. CYP2C19 is predominantly involved in lansoprazole metabolism in EMs.
Abstract: AIMS: Lansoprazole is a substrate of CYP2C19 and CYP3A. The aim of this study was to compare the inhibitory effects of clarithromycin, an inhibitor of CYP3A on the metabolism of lansoprazole between CYP2C19 genotypes. METHODS: A two-way randomized double-blind, placebo-controlled crossover study was performed. Eighteen volunteers, of whom six were homozygous extensive metabolizers (EMs), six were heterozygous EMs and six were poor metabolizers (PMs) for CYP2C19, received two 6-day courses of either clarithromycin 800 mg or placebo daily in a randomized fashion with a single oral dose of lansoprazole 60 mg on day 6 in all cases. Plasma concentrations of lansoprazole and its metabolites, 5-hydroxylansoprazole and lansoprazole sulphone were monitored up to 24 h after dosing. RESULTS: During placebo administration, the mean AUC0, infinity of lansoprazole in homozygous EMs, heterozygous EMs and PMs were 4652 (95% CI, 2294, 7009) ng ml(-1) h, 8299 (4784, 11814) ng ml(-1) h and 25293 (17643, 32943) ng ml(-1) h (P < 0.001), respectively. Clarithromycin treatment significantly increased Cmax by 1.47-fold, 1.71-fold and 1.52-fold and AUC0, infinity of lansoprazole by 1.55-fold, 1.74-fold, and 1.80-fold in these genotype groups, respectively, whereas elimination half-life was prolonged only in PMs. The clarithromycin-mediated percent increase in pharmacokinetic parameters such as Cmax, AUC0, infinity or elimination half-life did not differ between the three CYP2C19 genotypes. CONCLUSIONS: The present study indicates that there are significant drug interactions between lansoprazole and clarithromycin in all CYP2C19 genotype groups probably through CYP3A inhibition. The bioavailability of lansoprazole might, to some extent, be increased through inhibition of P-glycoprotein during clarithromycin treatment.
Abstract: OBJECTIVE: Omeprazole, lansoprazole and rabeprazole have been widely used as proton pump inhibitors (PPIs). They can be metabolized in the liver by CYP2C19, a polymorphic enzyme, and have a wide inter-individual variability with respect to drug response. In the investigation reported here, we examined the kinetic characteristics of the three PPIs in healthy Chinese subjects in relation to CYP2C19 genotype status. METHODS: Six homozygous extensive metabolizers (homEMs), six heterozygous extensive metabolizers (hetEMs) and six poor metabolizers (PMs) were recruited for the study from a total of 90 healthy Chinese volunteers whose CYP2C19 genotype status was determined by means of PCR-restriction fragment length polymorphism (RFLP). The study was had an open label, randomized, three-way crossover design. After a single oral dose of 40 mg omeprazole, 30 mg lansoprazole or 40 mg rabeprazole, plasma concentrations of the three PPIs were determined by HPLC. RESULTS: There were some differences for the area under the plasma concentration-time curve (AUC), the elimination half-life (t(1/2 ke)) and the maximum plasma concentration (c(max)) in the three groups. In the homEMs, hetEMs and PMs, the relative AUC(0-infinity) values were 1:2.8:7.5 for omeprazole, 1:1.7:4.0 for lansoprazole and 1:1.6:3.7 for rabeprazole, respectively; the relative t(1/2 ke) values were 1:1.02:1.65 for omeprazole, 1:1.08:2.39 for lansoprazole and 1:1.37:1.85 for rabeprazole, respectively; the relative c(max) values were 1:2.09:4.39 for omeprazole, 1:1.34:1.72 for lansoprazole, and 1:1.24:2.04 for rabeprazole, respectively. CONCLUSION: The pharmacokinetic characteristics of the three PPIs are significantly dependent on the CYP2C19 genotype status. These data indicate that individualized dose regimen of the three PPIs, based on identification of genotype, can be of great benefit for ensuring the reasonable use of these drugs.
Abstract: Use of in vitro suspensions of human hepatocytes is currently accepted as one of the most promising tools for prediction of metabolic clearance in new drugs. The possibility of creating computational models based on this data may potentiate the early selection process of new drugs. We present an artificial neural network for modelling human hepatocyte intrinsic clearances (CL(int)) based only on calculated molecular descriptors. In vitro CL(int) data obtained in human hepatocytes suspensions was divided into a train group of 71 drugs for network optimization and a test group of another 18 drugs for early-stop and internal validation resulting in correlations of 0.953 and 0.804 for the train and test group respectively. The model applicability was tested with 112 drugs by comparing the in silico predicted CL(int) with the in vivo CL(int) estimated by the "well-stirred" model based on the in vivo hepatic clearance (CL(H)). Acceptable correlations were observed with r values of 0.508 and 63% of drugs within a 10-fold difference when considering blood binding in acidic drugs only. This model may be a valuable tool for prediction and simulation in the drug development process, allowing the in silico estimation of the human in vivo hepatic clearance.
Abstract: No Abstract available
Abstract: BACKGROUND: Anticholinergic drugs are often involved in explicit criteria for inappropriate prescribing in older adults. Several scales were developed for screening of anticholinergic drugs and estimation of the anticholinergic burden. However, variation exists in scale development, in the selection of anticholinergic drugs, and the evaluation of their anticholinergic load. This study aims to systematically review existing anticholinergic risk scales, and to develop a uniform list of anticholinergic drugs differentiating for anticholinergic potency. METHODS: We performed a systematic search in MEDLINE. Studies were included if provided (1) a finite list of anticholinergic drugs; (2) a grading score of anticholinergic potency and, (3) a validation in a clinical or experimental setting. We listed anticholinergic drugs for which there was agreement in the different scales. In case of discrepancies between scores we used a reputed reference source (Martindale: The Complete Drug Reference®) to take a final decision about the anticholinergic activity of the drug. RESULTS: We included seven risk scales, and evaluated 225 different drugs. Hundred drugs were listed as having clinically relevant anticholinergic properties (47 high potency and 53 low potency), to be included in screening software for anticholinergic burden. CONCLUSION: Considerable variation exists among anticholinergic risk scales, in terms of selection of specific drugs, as well as of grading of anticholinergic potency. Our selection of 100 drugs with clinically relevant anticholinergic properties needs to be supplemented with validated information on dosing and route of administration for a full estimation of the anticholinergic burden in poly-medicated older adults.
Abstract: 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.