Author(s) |
Turner, Joseph V
Maddalena, Desmond J
Agatonovic-Kustrin, Snezana
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Publication Date |
2004-01
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Abstract |
Purpose. Radial basis function artificial neural networks and theoretical descriptors were used to develop a quantitative structure– pharmacokinetic relationship for structurally diverse drug compounds.
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Methods. Human bioavailability values were taken from the literature and descriptors were generated from the drug structures. All models were trained with 137 compounds and tested with a further 15, after which they were evaluated for predictive ability with an additional 15 compounds.
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Results. The final model possessed a 10-31-1 topology and training and testing correlation coefficients were 0.736 and 0.897, respectively. Predictions for independent compounds agreed well with experimental literature values, especially for compounds that were well absorbed and/or had high observed bioavailability. Important theoretical descriptors included solubility parameters, electronic descriptors, and topological indices.
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Conclusions. Useful information regarding drug bioavailability was gained from drug structure alone, reducing the need for experimental methods in drug development.
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Citation |
Pharmaceutical Research, 21(1), p. 68-82
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ISSN |
1573-904X
0724-8741
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Link | |
Publisher |
Springer New York LLC
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Title |
Bioavailability Prediction Based on Molecular Structure for a Diverse Series of Drugs
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Type of document |
Journal Article
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Entity Type |
Publication
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