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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
ABSTRACT - Internet Electron. J. Mol. Des. February 2005, Volume 4, Number 2, 124-150

Prediction of Intestinal Epithelial Transport of Drug in (Caco-2) Cell Culture from Molecular Structure using in silico Approaches During Early Drug Discovery
Yovani Marrero Ponce, Miguel A. Cabrera Pérez, Vicente Romero Zaldivar, Marival Bermejo Sanz, Dany Siverio Mota, and Francisco Torrens
Internet Electron. J. Mol. Des. 2005, 4, 124-150

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Abstract:
The high interest in the prediction of the intestinal absorption for new chemical entities is generated by the increasing rate in the synthesis of compounds by combinatorial chemistry and the extensive cost of the traditional evaluation methods. Novel molecular descriptors have been applied to estimate the intestinal epithelial transport of drug in Caco-2 cell culture. Total and local (atom and atom-type) quadratic indices used in this study were calculated by TOMOCOMD-CARDD software. Linear Discriminant Analysis (LDA) was used to obtain a quantitative model that discriminates the high absorption compounds (P ≥ 8×10-6 cm/s) from those with moderate-poor absorption (P < 8×10-6 cm/s). A data set of 134 diverse structure drugs and two series of drugs-like compounds (12 compounds) were used as training and test set, respectively. In addition, Multiple Linear Regression (MLR) has been carried out to derive QSPerR models. All statistical analyses were performed with the STATISTICA software package. The obtained LDA model classified correctly 81.13% of compounds with moderate-poor absorption properties and the 96.30% of compounds with high absorption, showing a global good classification of 90.30% in the training set. The model showed a high Matthews' correlation coefficient (MCC = 0.80). Internal and external validation processes to demonstrate the robustness and predictive power of the obtained model were carried out. In this sense, the model classified correctly 87.31% (MCC = 0.73) in the leave-one-out cross-validation procedure. The discriminant model was also assessed by a 10-fold full cross-validation (removing approximately 13 compounds in each cycle, 85.82% of good classification), yielding a MCC of 0.70. Also this model shown an 87.5, 85.6, 84.7, 85.0, 85.3, 83.5, 84.1, 86.2, 85.9 and 85.9% of global good classification when n varied from 2 to 11 in the leave-n-out cross validation procedure. The model was stabilized around 85.9% when n was > 9. In addition, a data set of 7 HIV protease inhibitors (4 linear peptidomimetic and 3 new cyclic urea) and 5 new 6-fluoroquinolones derivatives was used as external test set. The LDA-QSPerR model achieved a MCC of 0.71 (83.33% correct prediction) in this study. This approach permits us to obtain a good explanation of the experiment based on the molecular structural features, evidencing the main role of H-bonding and size properties in permeability process. Finally, the model developed was used in the virtual screening of 241 drugs with the percentage of human intestinal absorption (Abs %) values reported. A relationship between the predicted permeability coefficients in Caco-2 and the Abs % (145 compounds with good data quality) was established, with a percentage of good relation greater than 82 %. A comparison with results derived from other three theoretical studies shown a quite satisfactory behavior of the present method. All these results shown that total and local (atom and atom-type) quadratic indices can successfully predict intestinal permeability and suggest that the proposed methodology will be a good tool for studying the oral absorption of drug candidates during the drug development process.

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