Toward the establishment of a standardized pre-clinical porcine model to predict food effects - case studies on Fenofibrate and Paracetamol
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Published version
Date
2019-05-28
Authors
Henze, Laura J.
Koehl, Niklas J.
O'Shea, Joseph P.
Holm, René
Vertzoni, Maria
Griffin, Brendan T.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Published Version
Abstract
A preclinical porcine model that reliably predicts human food effect of fenofibrate was developed. Fenofibrate was administered to pigs as model compound with a positive food effect. Two different types of fed conditions were explored: a FDA style breakfast and a standard pig pellet feed. In order to assess if complete stomach emptying had been achieved under the employed fasting protocol, the amount of gastric and intestinal content was evaluated post-mortem. In addition, the protocol was designed to evaluate gastric emptying in the pre- and postprandial state using paracetamol as a marker. The study confirmed that micronized fenofibrate displayed a positive food effect with a similar fold difference to humans in FDA style fed state. Post-mortem assessment of stomach and intestinal content confirmed significantly lower content in the fasted compared to the pig pellet fed state. In the case of paracetamol, a delayed gastric emptying in the fed state was not observed, which may suggest that the Magenstrasse phenomena reported in humans, may also occur in landrace pigs. The study demonstrated the utility of a food effect protocol in landrace pigs as a pre-clinical approach to predict human food effects and provided new insights into gastric emptying in pigs.
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Keywords
Landrace pigs , Minipigs , Food effect , Fasting protocol , Gastric emptying
Citation
Henze, L. J., Koehl, N. J., O'Shea, J. P., Holm, R., Vertzoni, M. and Griffin, B. T. (2019) 'Toward the establishment of a standardized pre-clinical porcine model to predict food effects – Case studies on fenofibrate and paracetamol', International Journal of Pharmaceutics: X, 1, pp. 100017. doi: 10.1016/j.ijpx.2019.100017