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Transl Clin Pharmacol. 2015 Dec;23(2):66-74. English. Original Article.
Lee J , Lim MS , Seong SJ , Park SM , Gwon MR , Han S , Lee SM , Kim W , Yoon YR , Yoo HD .
Clinical Trial Center, Department of Biomedical Science and BK21 Plus Program, Kyungpook National University Hospital and School, Daegu 41944, Republic of Korea.
College of Pharmacy, Yeungnam University, Gyeongsan 42415, Korea.
PIPET (Pharmacometrics Institute for Practical Education and Training), The Catholic University of Korea, Seoul 06591, Republic of Korea.
Department of Pharmacology, Kosin University College of Medicine, Busan 49267, Korea.
Department of Biostatistics and Bioinformatics, Pharma Partnering Inc., Seoul 06735, Republic of Korea.

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.

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