4.5 Article

A Sensitive Medium-Throughput Method to Predict Intestinal Absorption in Humans Using Rat Intestinal Tissue Segments

期刊

JOURNAL OF PHARMACEUTICAL SCIENCES
卷 104, 期 9, 页码 2807-2812

出版社

WILEY
DOI: 10.1002/jps.24372

关键词

Biopharmaceutics classification system (BCS); ex vivo; in vitro models; intestinal absorption; permeability; rat intestine segments

资金

  1. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)
  2. Coordenacao de Aperfeicoamento Pessoal de Nivel Superior (CAPES, Brazil)
  3. Fundacao de Amparo a Pesquisa do Estado de Goias
  4. Rede Pro Centro-Oeste

向作者/读者索取更多资源

A range of in vitro, ex vivo, and in vivo approaches are currently used for drug development. Highly predictive human intestinal absorption models remain lagging behind the times because of numerous variables concerning permeability through gastrointestinal tract in humans. However, there is a clear need for a drug permeability model early in the drug development process that can balance the requirements for high throughput and effective predictive potential. The present study developed a medium throughput screening Snapwell (MTS-Snapwell) ex vivo model to provide an alternative method to classify drug permeability. Rat small intestine tissue segments were mounted in commercial Snapwell inserts. Unidirectional drug transport (A-B) was measured by collecting samples at different time points. Viability of intestinal tissue segments was measured by examining transepithelial electric resistance (TEER) and phenol red and caffeine transport. As a result, the apparent permeability (P-app; x10(-6) cm/s) was determined for atenolol (10.7 +/- 1.2), caffeine (17.6 +/- 3.1), cimetidine (6.9 +/- 0.1), metoprolol (12.6 +/- 0.7), theophylline (15.3 +/- 1.6) and, ranitidine (3.8 +/- 0.4). All drugs were classified in high/low permeability according to Biopharmaceutics Classification System showing high correlation with human data (r = 0.89). These findings showed a high correlation with human data (r = 0.89), suggesting that this model has potential predictive capacity for paracellular and transcellular passively absorbed molecules. (c) 2015 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:2807-2812, 2015

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