Evaluation of the Predicted Time-Concentration Profile of Serum Tulobuterol in Human after Transdermal Application

We proposed an in vitro/in vivo/in silico method for evaluating the clinical performance of matrix type transdermal therapeutic systems (TTSs). This method is based on the following four approaches: (1) drug release experiment, (2) in vitro penetration experiment using excised hairless mouse skin, (...

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Veröffentlicht in:Chemical & pharmaceutical bulletin 2012/03/01, Vol.60(3), pp.300-305
Hauptverfasser: Nakamura, Akinori, Mori, Daisuke, Tojo, Kakuji
Format: Artikel
Sprache:eng
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Zusammenfassung:We proposed an in vitro/in vivo/in silico method for evaluating the clinical performance of matrix type transdermal therapeutic systems (TTSs). This method is based on the following four approaches: (1) drug release experiment, (2) in vitro penetration experiment using excised hairless mouse skin, (3) clinical pharmacokinetic study, and (4) mathematical model for evaluating the pharmacokinetic profile. The tulobuterol TTS was used as an example of a matrix type TTS in this study. The drug diffusion coefficient in the matrix device was calculated from the result of the release experiment. The drug diffusion coefficient and the partition coefficient in the skin were calculated from the results of in vitro skin penetration experiments where hairless mice and rats were used. Those parameters were used as substitutes of human. Further, these parameters were used for solving the governing partial differential equation on skin penetration. The time profiles of the serum concentration in human after applying the tulobuterol TTS were predicted and compared with the clinical data. The predicted profiles obtained from the data of hairless mice reproduced the influence of drug depletion adequately and well agreed with the clinical data, while those from the data of rats differed clearly in the initial rise. This method is useful for prediction of pharmacokinetic profiles of TTSs.
ISSN:0009-2363
1347-5223
DOI:10.1248/cpb.60.300