title: A comparison of model performance for six quantitative structure-activity relationship packages that predict acute toxicity to fish.
abstract: Some regulatory programs rely on quantitative structure-activity relationship (QSAR) models to predict toxic effects to biota. Many currently existing QSAR models can predict the effects of a wide range of substances to biota, particularly aquatic biota. The difficulty for regulatory programs is in choosing the appropriate QSAR model or models for application in their new and existing substances programs. We evaluated model performance of six QSAR modeling packages: Ecological Structure Activity Relationship (ECOSAR), TOPKAT, a Probabilistic Neural Network (PNN), a Computational Neural Network (CNN), the QSAR components of the Assessment Tools for the Evaluation of Risk (ASTER) system, and the Optimized Approach Based on Structural Indices Set (OASIS) system. Using a testing data set of 130 substances that had not been included in the training data sets of the QSAR models under consideration, we compared model predictions for 96-h median lethal concentrations (LC50s) to fathead minnows to the corresponding measured toxicity values available in the AQUIRE database. The testing data set was heavily weighted with neutral organics of low molecular weight and functionality. Many of the testing data set substances also had a nonpolar narcosis mode of action and/or were chlorinated. A variety of statistical measures (correlation coefficient, slope and intercept from a linear regression analysis, mean absolute and squared difference between log prediction and log measured toxicity, and the percentage of predictions within factors of 2, 5, 10, 100, and 1,000 of measured toxicity values) indicated that the PNN model had the best model performance for the full testing data set of 130 substances. The rank order of the remainder of the models depended on the statistical measure employed. TOPKAT also had excellent model performance for substances within its optimum prediction space. Only 37% of the substances in the testing data set, however, fell within this optimum prediction space.
authors.affiliation: The Cadmus Group, Suite 204, 411 Roosevelt Avenue, Ottawa, Ontario K2A 3X9, Canada. dmoore@cadmusgroup.com
authors.forename: Dwayne R J
authors.initials: DR
authors.lastname: Moore
authors.forename: Roger L
authors.initials: RL
authors.lastname: Breton
authors.forename: Drew B
authors.initials: DB
authors.lastname: MacDonald
mesh_terms: D000818:Animals; D005399:Fishes; D005544:Forecasting; D007928:Lethal Dose 50; D008962:Models, Theoretical; D008970:Molecular Weight; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000633:toxicity; D021281:Quantitative Structure-Activity Relationship; D018570:Risk Assessment; D014874:Water Pollutants, Chemical / Q000633:toxicity*
chemical_list: D009930:Organic Chemicals; D014874:Water Pollutants, Chemical