&lt;p&gt;Population pharmacokinetic analysis of tramadol and &lt;em&gt;O&lt;/em&gt;-desmethyltramadol with genetic polymorphism of &lt;em&gt;CYP2D6&lt;/em&gt;&lt;/p&gt;

Lee, Joomi; Yoo, Hee-Doo; Bae, Jung‐Woo; Lee, Sooyeun; Shin, Kyung‐Jae

doi:10.2147/dddt.s199574

Cited by 15 publications

(15 citation statements)

References 24 publications

(35 reference statements)

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“…A first-order absorption constant best described tramadol absorption in this model. The estimated value in this study is considered higher compared to previous published models [15,16,34]. One explanation for this difference could be related to the age of the cohort included in this study.…”

Section: Discussioncontrasting

confidence: 67%

“…ISAR and CrCL were the only covariates included in the final model, where a higher value for the ISAR decreased The two-compartment model that was used to describe the tramadol data is similar to previously reported models describing tramadol pharmacokinetic parameters in a paediatric population [14]. However, other models have used between one and five compartments to describe the tramadol concentration data [15,16]. These referenced models investigated the pharmacokinetics of tramadol in breast milk or the effect of CYP2D6 genotyping on tramadol pharmacokinetic parameters.…”

Section: Discussionmentioning

confidence: 85%

“…Understanding the pharmacokinetics and pharmacodynamics of tramadol and ODT after an extended release oral dosing may aid appropriate use in older patients regarding appropriate drug selection and dosing [13]. Numerous PopPK models for tramadol and its active metabolites have been reported where pharmacokinetic parameters have been described in different cohorts of patients [14][15][16], but none have focussed on older patients.…”

Section: Introductionmentioning

confidence: 99%

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Population Pharmacokinetic Model for Tramadol and O-desmethyltramadol in Older Patients

Al-Qurain

Upton

Tadros

et al. 2022

Eur J Drug Metab Pharmacokinet

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Background and ObjectivesTramadol is commonly prescribed to manage chronic pain in older patients. However, there is a gap in the literature describing the pharmacokinetic parameters for tramadol and its active metabolite (O-desmethyltramadol [ODT]) in this population. The objective of this study was to develop and evaluate a population pharmacokinetic model for tramadol and ODT in older patients. Methods Twenty-one patients who received an extended-release oral tramadol dose (25-100 mg) were recruited. Tramadol and ODT concentrations were determined using a validated liquid chromatography/tandem mass spectrometry method. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. The performance of the model was assessed by visual predictive check. Results A two-compartment, first-order absorption model with linear elimination best described the tramadol concentration data. The absorption rate constant was 2.96/h (between-subject variability [BSV] 37.8%), apparent volume of distribution for the central compartment (V 1 /F) was 0.373 l (73.8%), apparent volume of distribution for the peripheral compartment (V 2 /F) was 0.379 l (97.4%), inter-compartmental clearance (Q) was 0.0426 l/h (2.19%) and apparent clearance (CL/F) was 0.00604 l/h (6.61%). The apparent rate of metabolism of tramadol to ODT (k t ) was 0.0492 l/h (78.5%) and apparent clearance for ODT (CL m ) was 0.143 l/h (21.6%). Identification of Seniors at Risk score (ISAR) and creatinine clearance (CrCL) were the only covariates included in the final model, where a higher value for the ISAR increased the maximum concentration (C max ) of tramadol and reduced the BSV in Q from 4.71 to 2.19%. A higher value of CrCL reduced tramadol C max and halflife (T 1/2 ) and reduced the BSV in V 2 /F (from 148 to 97.4%) and in CL/F (from 78.9 to 6.61%). Conclusion Exposure to tramadol increased with increased frailty and reduced CrCL. Prescribers should consider patients frailty status and CrCL to minimise the risk of tramadol toxicity in such cohort of patients.

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Section: Discussioncontrasting

confidence: 67%

Section: Discussionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

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Population Pharmacokinetic Model for Tramadol and O-desmethyltramadol in Older Patients

Al-Qurain

Upton

Tadros

et al. 2022

Eur J Drug Metab Pharmacokinet

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“…Whole blood was collected in an anticoagulant tube at pre-dose, and at 0.5, 1, 1.5, 2, 2.5, 4, 6, 8, 10, 12, 24, 48, and 72 h after administration. The obtained whole blood was used for CYP2D6 genotyping, and the plasma was separated for determination of tramadol and M1 [17].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the Effect of CYP2D6 Genotypes on Tramadol and O-Desmethyltramadol Pharmacokinetic Profiles in a Korean Population Using Physiologically-Based Pharmacokinetic Modeling

Jeong

Bae

et al. 2019

Pharmaceutics

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Tramadol is a μ-opioid receptor agonist and a monoamine reuptake inhibitor. O-desmethyltramadol (M1), the major active metabolite of tramadol, is produced by CYP2D6. A physiologically-based pharmacokinetic model was developed to predict changes in time-concentration profiles for tramadol and M1 according to dosage and CYP2D6 genotypes in the Korean population. Parallel artificial membrane permeation assay was performed to determine tramadol permeability, and the metabolic clearance of M1 was determined using human liver microsomes. Clinical study data were used to develop the model. Other physicochemical and pharmacokinetic parameters were obtained from the literature. Simulations for plasma concentrations of tramadol and M1 (after 100 mg tramadol was administered five times at 12-h intervals) were based on a total of 1000 virtual healthy Koreans using SimCYP® simulator. Geometric mean ratios (90% confidence intervals) (predicted/observed) for maximum plasma concentration at steady-state (Cmax,ss) and area under the curve at steady-state (AUClast,ss) were 0.79 (0.69–0.91) and 1.04 (0.85–1.28) for tramadol, and 0.63 (0.51–0.79) and 0.67 (0.54–0.84) for M1, respectively. The predicted time–concentration profiles of tramadol fitted well to observed profiles and those of M1 showed under-prediction. The developed model could be applied to predict concentration-dependent toxicities according to CYP2D6 genotypes and also, CYP2D6-related drug interactions.

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“…Selbst scheinbar "ungefährliche" Phytotherapeutika wie Cranberry-Produkte, die häufig in der Selbstmedikation jenseits des ärztlichen Verordnungshorizontes eingenommen werden, können auf der Zytochromebene fatale Folgen wie die Unwirksamkeit einer immunsuppressiven Therapie etwa mit Tacrolimus haben [8]. Ein anderes Beispiel ist die Abhängigkeit der Metabolisierung des schwachen Opioids Tramadol zu dem eigentlich wirksamen Wirkstoff Desmethyltramadol über die P 450 2D6-Oxidase, welche bei Hemmung durch Inhibitoren in der Comedikation oder bei genetisch bedingter Insuffizienz zu einer Wirkabschwächung führt [9].…”

Section: Introductionunclassified

Unerwünschte Arzneimittelwirkungen am Harntrakt – der Wittener Harntrakt-Nebenwirkungs-Score

et al. 2021

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ZusammenfassungObwohl der Harntrakt mit Restharnbildung, Harnverhaltung, Pollakisurie, Polyurie, Nykturie, Detrusorstimulation, Detrusorhemmung, Hämaturie, Dysurie und anderen Erscheinungen häufig den Schauplatz unerwünschter Arzneimittelwirkungen darstellt, fehlt es an einer Gesamtübersicht an Substanzen, die eine solche Harntraktnebenwirkung auslösen können und eine entsprechende Bewertung. Vorhandene Listen „potenziell inadäquater Medikation“ fokussieren entweder auf eine pharmakologische Gruppe von Nebenwirkungen („anticholinergic burden score“), eine Gruppe von Medikamenten bestimmter Indikation (LUTS-Forta) oder auf eine selektionierte Gruppe von Patienten (PRISKUS-Liste, beers-Liste).Mit dem folgenden interdisziplinären Projekt aus der Arbeitsgruppe Uro-Geriatrie der Universität Witten/Herdecke sollte diese Lücke geschlossen werden. Es erfolgte eine Identifikation von Substanzen, die eine Harntraktnebenwirkung lt. diverser Datenbanken prinzipiell auslösen können, eine Kategorisierung der hierzu vorhandenen Literatur (Kasuistik, Fallsammlung, RCT, Metaanalyse) und eine strukturierte Bewertung des Risikos durch 33 Experten. Das Ergebnis stellt eine Liste von 235 Substanzen dar, die zu verschiedenen Harntraktnebenwirkungen führen können. Diese Liste enthält einen die Nennung in Datenbanken bzw. deren Korrelat in der Literatur darstellenden „theroretischen“ Punktwert, einen die klinische Realität abbildenden „praktischen“ Punktwert, der die Expertenbewertung darstellt, und einen Summenscore – geordnet nach der Systematik der „Roten Liste“.Erstmals wäre damit sowohl bei der Neuverordnung einer Substanz vor dem Hintergrund bestehender patientenseitiger Risiken eine Einschätzung dieses Wirkstoffes im Hinblick auf Harntraktnebenwirkungen möglich; andersherum könnte die Durchsicht des Medikationsplanes bei Vorhandensein einer Harntraktfunktionsstörung die Frage klären, ob diese medikamentös (mit-) verursacht ist. Die Entwicklung des „Harntraktnebenwirkungs-Rechners“ als „App“ ist geplant.

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<p>Population pharmacokinetic analysis of tramadol and <em>O</em>-desmethyltramadol with genetic polymorphism of <em>CYP2D6</em></p>

Cited by 15 publications

References 24 publications

Population Pharmacokinetic Model for Tramadol and O-desmethyltramadol in Older Patients

Population Pharmacokinetic Model for Tramadol and O-desmethyltramadol in Older Patients

Evaluation of the Effect of CYP2D6 Genotypes on Tramadol and O-Desmethyltramadol Pharmacokinetic Profiles in a Korean Population Using Physiologically-Based Pharmacokinetic Modeling

Unerwünschte Arzneimittelwirkungen am Harntrakt – der Wittener Harntrakt-Nebenwirkungs-Score

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