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Received June 13, 2007
Accepted January 14, 2008
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Aqueous solubility of poorly water-soluble drugs: Prediction using similarity and quantitative structure-property relationship models

1Insilicotech Co. Ltd., A-1101 Kolontripolis, 210, Geumgok-dong, Bundang-gu, Seongnam-shi 463-943, Korea 2Interdisciplinary Program in Powder Technology, Graduate School, Pusan National University, 30, Jangjeon-dong, Geumjeong-gu, Busan 609-735, Korea 3Department of Pharmaceutical Manufacturing, Pusan National University, 30, Jangjeon-dong, Geumjeong-gu, Busan 609-735, Korea
wschoi@pusan.ac.kr
Korean Journal of Chemical Engineering, July 2008, 25(4), 865-873(9), 10.1007/s11814-008-0143-x
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Abstract

The aqueous solubility of poorly water-soluble drugs is an important property of many factors affecting their bioavailability such as the solubility and rate of dissolution in water. The quantitative structure-property relationship approach using genetic algorithm was applied to make models for predicting some poorly water-soluble drugs such as ursodeoxycholic acid, diphenyl hydrantoin and biphenyl dimethyl dicarboxylate. The experimental solubility data of 3518 chemical structures were collected from the web and used to build a model. Three data sets of 50 compounds were extracted according to their structural similarity with each drug. A fast and predictive similarity based approach was developed and validated with conventional method. This can be used to predict the aqueous solubility for drugs by using a small set of compounds, especially for poorly water-soluble compounds. Moreover, the estimation values of various sets were further compared with fine grinding experiment data.

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