Use of quantitative structural analysis to predict fish bioconcentration factors for pesticides.
The focus of this research was to develop a model based solely on molecular descriptors capable of predicting fish bioconcentration factors (BCF). A fish BCF database was developed from high-quality, regulatory agency reviewed studies for pesticides based on the same laboratory protocol and the same fish species, Lepomis macrochirus. A commercially available software program was used to create a quantitative structure-activity relationship (QSAR) from 93 BCF studies based on unique molecules. An additional 16 molecules were used to test the accuracy of QSAR model predictions for a variety of pesticide classes. Regression of the measured versus predicted log BCF values yielded a regression coefficient of 0.88 for the validation data set. On the basis of the results from this research, the ability to predict BCF by a QSAR regression model is improved using a fully structurally derived model based solely on structural data such as the number of atoms for a given group (e.g., -CH3) or the local topology of each atom as derived from electron counts. Such descriptors provide insightful information on a molecule's potential BCF behavior in aquatic systems.
Jackson
Scott H
SH
BASF Corporation, Research Triangle Park, North Carolina 27709, USA.
Cowan-Ellsberry
Christina E
CE
Thomas
Gareth
G
D000818:Animals; D005399:Fishes / Q000378:metabolism*; D008660:Metabolism; D010473:Perciformes / Q000378:metabolism; D010575:Pesticides / Q000737:chemistry* / Q000378:metabolism* / Q000493:pharmacokinetics; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
D010575:Pesticides