Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/106232
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dc.titlePopulation pharmacokinetics of tacrolimus in whole blood and plasma in Asian liver transplant patients
dc.contributor.authorWai, J.S.
dc.contributor.authorLai, S.T.
dc.contributor.authorHolmes, M.J.
dc.contributor.authorAw, M.
dc.contributor.authorSeng, H.Q.
dc.contributor.authorKang, H.L.
dc.contributor.authorSeng, G.L.
dc.contributor.authorPrabhakaran, K.
dc.contributor.authorSui, Y.C.
dc.contributor.authorHo, P.C.
dc.date.accessioned2014-10-29T01:57:04Z
dc.date.available2014-10-29T01:57:04Z
dc.date.issued2006
dc.identifier.citationWai, J.S., Lai, S.T., Holmes, M.J., Aw, M., Seng, H.Q., Kang, H.L., Seng, G.L., Prabhakaran, K., Sui, Y.C., Ho, P.C. (2006). Population pharmacokinetics of tacrolimus in whole blood and plasma in Asian liver transplant patients. Clinical Pharmacokinetics 45 (1) : 59-75. ScholarBank@NUS Repository.
dc.identifier.issn03125963
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/106232
dc.description.abstractObjectives: The objectives of this study were to develop population pharmacokinetic models of tacrolimus in an Asian population with whole blood and plasma drug concentration data, to compare the variability of the pharmacokinetic parameters in these two matrices and to search for the main patient characteristics that explain the variability in pharmacokinetic parameters. Study design: Prospective pharmacokinetic assessment followed by model fitting. Patients: Whole blood samples from 31 liver transplant patients in a local hospital receiving oral tacrolimus as part of their immunosuppressive therapy were assessed. Plasma samples from 29 of the 31 patients were also evaluated. Concentrations of tacrolimus in whole blood and plasma were determined by an electrospray high-performance liquid chromatography with tandem mass spectrometry. Two hundred and thirteen whole blood and 157 plasma tacrolimus concentrations were used for building two nonlinear mixed-effects population models to describe the disposition of tacrolimus in whole blood and plasma, respectively. Covariates that were investigated included demographic characteristics, biological markers of liver and renal functions, corticosteroid dose and haematological parameter. Results: A one-compartment model was used to describe the whole blood and plasma concentration-time data of tacrolimus after oral administration. For the whole blood population model, the population estimates of the first-order absorption rate constant (ka), apparent clearance based on whole blood concentration after oral administration (CL B/F) and apparent volume of distribution based on whole blood concentrations after oral administration (Vd,B/F) were 2.08h -1, 14.1 L/h and 217L, respectively. The coefficient of variations (CVs) of interpatient variabilities in CLB/F and Vd,B/F were 65.7% and 63.8%, respectively. Bodyweight, liver and renal function influenced CLB/F, while height and haematocrit influenced V d,B/F. The residual (unexplained) variability was 34.8%. For the plasma population model, the population estimates of the ka, apparent clearance based on plasma concentrations after oral administration (CLP/F) and apparent volume of distribution based on plasma concentrations after oral administration (Vd,P/F) were 5.21h-1, 537 L/h and 563L, respectively. The CVs of interpatient variabilities in CLP/F and Vd,P/F were 96.0% and 105.4%, respectively. Bodyweight was found to influence CLP/F, while the erythrocyte-to-plasma concentration ratio influenced Vd,P/F. The residual (unexplained) variability was 49.8% at the mean plasma concentration of 1.1 ng/mL. Conclusions: Whole blood and plasma population pharmacokinetic models of tacrolimus in Asian adult and paediatric liver transplant patients were developed using prospective data in a clinical setting. This has identified and quantified sources of interindividual variability in CLB/F, Vd,B/F, CLP/F and V d,P/F of tacrolimus in Asian liver transplant patients. Information derived from the whole blood population model may subsequently be used by clinicians for dosage individualisation through Bayesian forecasting. © 2006 Adis Data Information BV. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.2165/00003088-200645010-00004
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentPHARMACY
dc.description.sourcetitleClinical Pharmacokinetics
dc.description.volume45
dc.description.issue1
dc.description.page59-75
dc.description.codenCPKND
dc.identifier.isiut000235692800004
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