Bioavailability prediction based on molecular structure for a diverse series of drugs.

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. 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.

journal_name

Pharm Res

journal_title

Pharmaceutical research

authors

Turner JV,Maddalena DJ,Agatonovic-Kustrin S

doi

10.1023/b:pham.0000012154.09631.26

subject

Has Abstract

pub_date

2004-01-01 00:00:00

pages

68-82

issue

1

eissn

0724-8741

issn

1573-904X

journal_volume

21

pub_type

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