Bioavailability prediction based on molecular structure for a diverse series of drugs.
OBJECTIVE
Radial basis function artificial neural networks and theoretical descriptors were used to develop a quantitative structure-pharmacokinetic relationship for structurally diverse drug compounds.


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.


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.


CONCLUSIONS
Useful information regarding drug bioavailability was gained from drug structure alone, reducing the need for experimental methods in drug development.
Turner
Joseph V
JV
Faculty of Pharmacy, The University of Sydney, Sydney NSW 2006 Australia. s4050159@student.uq.edu.au
Maddalena
Desmond J
DJ
Agatonovic-Kustrin
Snezana
S
D000704:Analysis of Variance; D001682:Biological Availability; D019295:Computational Biology / Q000379:methods; D003627:Data Interpretation, Statistical; D006801:Humans; D015394:Molecular Structure; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism*; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric
D004364:Pharmaceutical Preparations