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Acta Pharmacol Sin. 2005 Nov;26(11):1322-33.

Predicting MDCK cell permeation coefficients of organic molecules using membrane-interaction QSAR analysis.

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State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing 210093, China.



To use membrane-interaction quantitative structure-activity relationship analysis (MI-QSAR) to develop predictive models of partitioning of organic compounds in gastrointestinal cells.


A training set of 22 structurally diverse compounds, whose apparent permeability across cellular membranes of Madin-Darby canine kidney (MDCK) cells were measured, were used to construct MI-QSAR models. Molecular dynamic simulations were used to determine the explicit interaction of each test compound (solute) with a dimyristoyl-phosphatidyl-choline monolayer membrane model. An additional set of intramolecular solute descriptors were computed and considered in the trial pool of descriptors for building MI-QSAR models. The QSAR models were optimized using multidimensional linear regression fitting and the stepwise method. A test set of 8 compounds were evaluated using the MI-QSAR models as part of a validation process.


MI-QSAR models of the gastrointestinal absorption process were constructed. The descriptors found in the best MI-QSAR models are as follows: 1) ClogP (the logarithm of the 1-octanol/water partition coefficient); 2) E(HOMO) (the highest occupied molecular orbital energy); 3) E(s) (stretch energy); 4) PM(Y) (the principal moment of inertia Y, the inertia along the y axis in the rectangular coordinates; 5) C(t) (total connectivity); and 6) E(nb) (the energy of interactions between all of the non-bonded atoms). The most important descriptor in the models is ClogP.


Permeability is not only determined by the properties of drug molecules, but is also very much influenced by the molecule-membrane interaction process.

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