학술논문
Population Pharmacokinetic Model for Tramadol and O-desmethyltramadol in Older Patients.
Document Type
Academic Journal
Author
Al-Qurain AA; UniSA: School of Pharmacy and Medical Science, Clinical and Health Sciences, University of South Australia, Adelaide, Australia. aymen.al_qurain@mymail.unisa.edu.au.; Pharmacy Department, Mohammed Almana College for Medical Sciences, Abdulrazaq Bin Hammam Street, Alsafa, Dammam, Saudi Arabia. aymen.al_qurain@mymail.unisa.edu.au.; Department of Health and Biomedical Innovation, University of South Australia, Adelaide, Australia. aymen.al_qurain@mymail.unisa.edu.au.; Upton RN; UniSA: School of Pharmacy and Medical Science, Clinical and Health Sciences, University of South Australia, Adelaide, Australia.; Tadros R; Department of Internal Medicine, Royal Adelaide Hospital, Adelaide, Australia.; Roberts MS; UniSA: School of Pharmacy and Medical Science, Clinical and Health Sciences, University of South Australia, Adelaide, Australia.; Department of Therapeutics Research Centre, Diamantina Institute, Translational Research Institute, The University of Queensland, Brisbane, Queensland, Australia.; Wiese MD; UniSA: School of Pharmacy and Medical Science, Clinical and Health Sciences, University of South Australia, Adelaide, Australia.; Department of Health and Biomedical Innovation, University of South Australia, Adelaide, Australia.
Source
Publisher: Springer France Country of Publication: France NLM ID: 7608491 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2107-0180 (Electronic) Linking ISSN: 03787966 NLM ISO Abbreviation: Eur J Drug Metab Pharmacokinet Subsets: MEDLINE
Subject
Language
English
Abstract
Background and Objectives: Tramadol 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 (V1 /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 half-life (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.
(© 2022. The Author(s).)
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
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.
(© 2022. The Author(s).)