Bioavailability Prediction Based on Molecular Structure for a Diverse Series of Drugs

Author(s)
Turner, Joseph V
Maddalena, Desmond J
Agatonovic-Kustrin, Snezana
Publication Date
2004-01
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. <br/> 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. <br/> 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. <br/> Conclusions. Useful information regarding drug bioavailability was gained from drug structure alone, reducing the need for experimental methods in drug development.
Citation
Pharmaceutical Research, 21(1), p. 68-82
ISSN
1573-904X
0724-8741
Link
Publisher
Springer New York LLC
Title
Bioavailability Prediction Based on Molecular Structure for a Diverse Series of Drugs
Type of document
Journal Article
Entity Type
Publication

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