Literature DB >> 16863428

Computational models to predict aqueous drug solubility, permeability and intestinal absorption.

Christel A S Bergström1.   

Abstract

In the last decade, poor intestinal absorption of candidate drugs intended for oral administration has been identified as a major bottleneck in drug development. Poor intestinal absorption can often be related to poor aqueous solubility and/or poor permeability across the intestinal wall. Other factors, such as poor stability and the metabolism of the compounds, can also decrease the amount of compound absorbed. In an effort to design compounds with enhanced absorption profile, theoretical predictions of solubility and permeability, among other factors, have gained increased interest, and a large number of papers have been published. In this review, the databases and techniques used for the development of in silico absorption models will be discussed. The focus is on aqueous drug solubility, which has become a major problem in drug development.

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Year:  2005        PMID: 16863428     DOI: 10.1517/17425255.1.4.613

Source DB:  PubMed          Journal:  Expert Opin Drug Metab Toxicol        ISSN: 1742-5255            Impact factor:   4.481


  4 in total

Review 1.  Predicting drug disposition, absorption/elimination/transporter interplay and the role of food on drug absorption.

Authors:  Joseph M Custodio; Chi-Yuan Wu; Leslie Z Benet
Journal:  Adv Drug Deliv Rev       Date:  2007-11-28       Impact factor: 15.470

2.  Estimating the Aqueous Solubility of Pharmaceutical Hydrates.

Authors:  Stephen J Franklin; Usir S Younis; Paul B Myrdal
Journal:  J Pharm Sci       Date:  2016-06       Impact factor: 3.534

3.  Three-class classification models of logS and logP derived by using GA-CG-SVM approach.

Authors:  Hui Zhang; Ming-Li Xiang; Chang-Ying Ma; Qi Huang; Wei Li; Yang Xie; Yu-Quan Wei; Sheng-Yong Yang
Journal:  Mol Divers       Date:  2009-01-31       Impact factor: 3.364

4.  ADME prediction with KNIME: In silico aqueous solubility consensus model based on supervised recursive random forest approaches.

Authors:  Gabriela Falcón-Cano; Christophe Molina; Miguel Ángel Cabrera-Pérez
Journal:  ADMET DMPK       Date:  2020-08-07
  4 in total

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