| Autor: |
Heuberger JA; Centre for Human Drug Research, PK/PD, Leiden, The Netherlands, julesheuberger@gmail.com., Guan Z, Oyetayo OO, Klumpers L, Morrison PD, Beumer TL, van Gerven JM, Cohen AF, Freijer J |
| Jazyk: |
angličtina |
| Zdroj: |
Clinical pharmacokinetics [Clin Pharmacokinet] 2015 Feb; Vol. 54 (2), pp. 209-19. |
| DOI: |
10.1007/s40262-014-0195-5 |
| Abstrakt: |
Δ(9)-Tetrahydrocannobinol (THC), the main psychoactive compound of Cannabis, is known to have a long terminal half-life. However, this characteristic is often ignored in pharmacokinetic (PK) studies of THC, which may affect the accuracy of predictions in different pharmacologic areas. For therapeutic use for example, it is important to accurately describe the terminal phase of THC to describe accumulation of the drug. In early clinical research, the THC challenge test can be optimized through more accurate predictions of the dosing sequence and the wash-out between occasions in a crossover setting, which is mainly determined by the terminal half-life of the compound. The purpose of this study is to better quantify the long-term pharmacokinetics of THC. A population-based PK model for THC was developed describing the profile up to 48 h after an oral, intravenous, and pulmonary dose of THC in humans. In contrast to earlier models, the current model integrates all three major administration routes and covers the long terminal phase of THC. Results show that THC has a fast initial and intermediate half-life, while the apparent terminal half-life is long (21.5 h), with a clearance of 38.8 L/h. Because the current model characterizes the long-term pharmacokinetics, it can be used to assess the accumulation of THC in a multiple-dose setting and to forecast concentration profiles of the drug under many different dosing regimens or administration routes. Additionally, this model could provide helpful insights into the THC challenge test used for the development of (novel) compounds targeting the cannabinoid system for different therapeutic applications and could improve decision making in future clinical trials. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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