Journal
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
Volume 43, Issue 11, Pages 2442-2452Publisher
ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.ejmech.2008.05.017
Keywords
Quantitative structure-activity relationships (QSAR); Bioavailability; Multiple linear regression (MLR); Leave-one-out cross-validation; Kohonen's self-organizing Neural Network (KohNN)
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Funding
- National Natural Science Foundation of China [20605003]
- National High Tech Project [2006AA02Z337]
- University of Chemical Technology
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Explorations into modeling human oral bioavailability started with a whole dataset of 772 drug compounds. First, training set and test set were chosen based on Kohonen's self-organizing Neural Network (KohNN). Then, a quantitative model of the whole dataset was built using multiple linear regression (MLR) analysis. This model had limited predictability emphasizing that a variety of pharmacokinetic factors influence human oral bioavailability. In order to explore whether better models can be built when the compounds share some ADME properties, four subsets were chosen from the whole dataset to build quantitative models and better models were obtained by MLR analysis. These studies show that, indeed, good models for predicting human oral bioavailability can be obtained from datasets sharing certain pharmacokinetic properties. (C) 2008 Elsevier Masson SAS. All rights reserved.
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