The goal of this work was to investigate the use of MDCK (Madin-Darby canine kidney) cells as a possible tool for assessing the membrane permeability properties of early drug discovery compounds. Apparent permeability (Papp) values of 55 compounds with known human absorption values were determined using MDCK cell monolayers. For comparison, Papp values of the same compounds were also determined using Caco-2 cells, a well-characterized in vitro model of intestinal drug absorption. Monolayers were grown on 0. 4-microm Transwell-COL membrane culture inserts. MDCK cells were seeded at high density and cultured for 3 days, and Caco-2 cells were cultured under standard conditions for 21 to 25 days. Compounds were tested using 100 microM donor solutions in transport medium (pH 7.4) containing 1% DMSO. The Papp values in MDCK cells correlated well with those in Caco-2 cells (r2 = 0.79). Spearman's rank correlation coefficient for MDCK Papp and human absorption was 0.58 compared with 0.54 for Caco-2 Papp and human absorption. These results indicate that MDCK cells may be a useful tool for rapid membrane permeability screening.
The absorption of a drug compound through the human intestinal cell lining is an important property for potential drug candidates. Measuring this property, however, can be costly and time-consuming. The use of quantitative structure-property relationships (QSPRs) to estimate percent human intestinal absorption (%HIA) is an attractive alternative to experimental measurements. A data set of 86 drug and drug-like compounds with measured values of %HIA taken from the literature was used to develop and test a QSPR mode. The compounds were encoded with calculated molecular structure descriptors. A nonlinear computational neural network model was developed by using the genetic algorithm with a neural network fitness evaluator. The calculated %HIA (cHIA) model performs wells, with root-mean-square (rms) errors of 9.4%HIA units for the training set, 19.7%HIA units for the cross-validation (CV) set, and 16.0%HIA units for the external prediction set.
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