Application of the MechPeff model to predict passive effective intestinal permeability in the different regions of the rodent small intestine and colon.

Abstract:

:A major component of physiologically based pharmacokinetic (PBPK) models is the prediction of the rate and extent of absorption of orally dosed drugs for which knowledge of effective passive intestinal permeability (Peff ) is essential. Single-pass intestinal perfusion (SPIP) studies are used to establish effective permeability in vivo but are difficult to perform in rodents, while mechanistic models to predict drug Peff in rat and mouse have not been published. This work evaluates the predictive performance of the 'MechPeff' model to predict Peff in the rodent intestine based upon knowledge of regional gut physiology and drug-specific physicochemical parameters. The 'MechPeff' model, built-in to the Simcyp Rat and Mouse Simulators, predicts transcellular, paracellular and mucus layer permeabilities and combines these to give the overall Peff . The jejunal and/or ileal Peff was predicted for 12 (4) acidic, 13 (12) basic, 10 (8) neutral and 2 (0) ampholytic drugs in the rat (mouse), spanning a wide range of MW and logPo:w , and compared with experimental Peff obtained using SPIP. A key input is the intrinsic transcellular permeability (Ptrans,0 ) which can be derived from modelling of appropriate in vitro permeability experiments or predicted from physicochemical properties. The Peff predictions were reasonably good when experimentally derived Ptrans,0 was used; from 42 Peff,rat values, 24 (57%) were within 3-fold, and of 19 Peff,mouse values, 12 (63%) were within 3-fold, of observed Peff . Considering the lack of alternative models to predict Peff in preclinical species, and the minimal drug-specific inputs required, this model provides a valuable tool within drug discovery and development programmes. Copyright © 2017 John Wiley & Sons, Ltd.

journal_name

Biopharm Drug Dispos

authors

Pade D,Jamei M,Rostami-Hodjegan A,Turner DB

doi

10.1002/bdd.2072

subject

Has Abstract

pub_date

2017-03-01 00:00:00

pages

94-114

issue

2

eissn

0142-2782

issn

1099-081X

journal_volume

38

pub_type

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