Development of models to predict fish early-life stage toxicity from acute Daphnia magna toxicity$.

Herein, we propose models for predicting fish early-life stage (ELS) toxicity from acute Daphnia magna toxicity and various molecular descriptors. Specifically, eight models were developed with fathead minnow (Pimephales promelas) data and were validated against Japanese medaka (Oryzias latipes) data because the quantity of available Japanese medaka data is much smaller than the quantity of fathead minnow data. The training data set for the models consisted of ELS fathead minnow toxicity data for 77 chemicals; data for 67 of the 77 chemicals originated from the OPP Pesticide Ecotoxicity Database of the US Environmental Protection Agency. The training data were biased toward pesticides. A simple quantitative activity-activity relationship (QAAR) model based on the correlation between fish ELS and acute Daphnia magna toxicities showed good predictivity for the chemicals in the external validation data set relative to the predictivities of the other models in this study. However, goodness-of-fit and robustness were better for quantitative structure-activity-activity relationship (QSAAR) models that included molecular descriptors (such as pesticide-related atoms and substructures as well as molecular weight and three-dimensional-structure-based parameters). A battery approach involving the use of both the QAAR and the QSAARs might enhance the reliability of the estimated values and prevent underestimates.