Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/10202
Title: A nonlinear mixed effects modelling analysis of topiramate pharmacokinetics in patients with epilepsy
Authors: Vovk, Tomaz
Jakovljevic, Mihajlo
Kerec Kos M.
Jankovic, Slobodan
Mrhar A.
Grabnar, Iztok
Journal: Biological and Pharmaceutical Bulletin
Issue Date: 16-Jul-2010
Abstract: Topiramate pharmacokinetics is influenced by individual factors such as patient age, renal function and co-treatment. The aim of this study was to develop a population pharmacokinetic model of topiramate to assist dosage adjustments in individual patients. Steady-state topiramate plasma concentrations in patients with epilepsy were determined by HPLC using fluorescent labelling. Demographic, biochemical data and dosing history including concomitant drug therapy were collected from patients' charts. Nonlinear mixed effects modelling was used to fit a one-compartment pharmacokinetic model. The influence of patient weight and gender, body surface area, age, creatinine clearance, serum transaminases, topiramate daily dose and co-treatment with carbamazepine, valproic acid, benzodiazepines, and risperidone on topiramate pharmacokinetics was evaluated. Additionally, the relationship between topiramate plasma concentration and clinical response was investigated. Volume of distribution of topiramate was 0.518 l/kg. For a typical patient oral clearance was estimated at 1.47 l/h, with interindividual variability of 39.2%. Clearance was 70% higher in patients co-treated with carbamazepine and was found to increase with patient age. Somnolence was the most frequently observed adverse event. Incidence of headache was associated with topiramate plasma concentration. Somnolence, ataxia, tremor, speech disorders and fatigue were associated with adjunctive therapy with carbamazepine, valproic acid, benzodiazepines, risperidone, and clozapine. No association of topiramate plasma concentration with frequency of seizures or patient quality of life was observed. The developed model can be used for Bayesian estimation of pharmacokinetic parameters based on sparse plasma samples and for selection of optimum dosing in routine patient care. © 2010 Pharmaceutical Society of Japan.
URI: https://scidar.kg.ac.rs/handle/123456789/10202
Type: Article
DOI: 10.1248/bpb.33.1176
ISSN: 09186158
SCOPUS: 77954480747
Appears in Collections:Faculty of Medical Sciences, Kragujevac
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