Population pharmacokinetic analysis of the multiple peaks phenomenon in sumatriptan.
10.12793/tcp.2015.23.2.66
- Author:
Joomi LEE
1
;
Mi Sun LIM
;
Sook Jin SEONG
;
Sung Min PARK
;
Mi Ri GWON
;
Seunghoon HAN
;
Sung Min LEE
;
Woomi KIM
;
Young Ran YOON
;
Hee Doo YOO
Author Information
1. Clinical Trial Center, Department of Biomedical Science and BK21 Plus Program, Kyungpook National University Hospital and School, Daegu 41944, Republic of Korea.
- Publication Type:Original Article
- Keywords:
Multiple peaks phenomenon;
NONMEM;
population pharmacokinetics;
sumatriptan
- MeSH:
Absorption;
Cerebral Arteries;
Creatinine;
Epilepsy;
Humans;
Male;
Migraine Disorders;
Plasma;
Serotonin;
Sumatriptan*;
Vasoconstriction
- From:Translational and Clinical Pharmacology
2015;23(2):66-74
- CountryRepublic of Korea
- Language:English
-
Abstract:
The objective of this study was to develop a population pharmacokinetic (PK) model for sumatriptan, which frequently shows an atypical absorption profile with multiple peaks. Sumatriptan, a selective agonist for the vascular serotonin (5-HT1) receptor that causes vasoconstriction of the cerebral arteries, is used for the acute treatment of migraine attack with or without aura. Despite its relatively high between-subject variability, few reports have addressed PK modeling of sumatriptan. Plasma data obtained after a single 50-mg oral dose of sumatriptan in 26 healthy Korean male subjects were used. Blood samples were collected 0 (predose), 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10, and 12 h after dosing. Plasma sumatriptan concentrations were analyzed using UPLC/MS/MS. Population PK analysis was performed using plasma concentration data for sumatriptan with NONMEM (ver. 7.2). A total of 364 concentrations of sumatriptan were captured by a one-compartment model with first-order elimination, and a combined transit compartment model and first-order absorption with lag time was successful in describing the PK with multiple peaks in the absorption phase of sumatriptan. The creatinine clearance as a covariate significantly (P < 0.01) influenced the absorption fraction (f ). The final model was validated through a visual predictive check and bootstrapping with no serious model misspecification.
