The authors developed a toxicity database for unionid mussels to examine the extent of intra- and interlaboratory variability in acute toxicity tests with mussel larvae (glochidia) and juveniles; the extent of differential sensitivity of the 2 life stages; and the variation in sensitivity among commonly tested mussels (Lampsilis siliquoidea, Utterbackia imbecillis, and Villosa iris), commonly tested cladocerans (Daphnia magna and Ceriodaphnia dubia), and fish (Oncorhynchus mykiss, Pimephales promelas, and Lepomis macrochirus). The results of these analyses indicate that intralaboratory variability for median effect concentrations (EC50) averaged about 2-fold for both life stages, whereas interlaboratory variability averaged 3.6-fold for juvenile mussels and 6.3-fold for glochidia. The EC50s for juveniles and glochidia were within a factor of 2 of each other for 50% of paired records across chemicals, with juveniles more sensitive than glochidia by more than 2-fold for 33% of the comparisons made between life stages. There was a high concurrence of sensitivity of commonly tested L. siliquoidea, U. imbecillis, and V. iris to that of other mussels. However, this concurrence decreased as the taxonomic distance of the commonly tested cladocerans and fish to mussels increased. The compiled mussel database and determination of data variability will advance risk assessments by including more robust species sensitivity distributions, interspecies correlation estimates, and availability of taxon-specific empirically derived application factors for risk assessment.
The ability to estimate aquatic toxicity is a critical need for ecological risk assessment and chemical regulation. The consensus in the literature is that mode of action (MOA) based toxicity models yield the most toxicologically meaningful and, theoretically, the most accurate results. In this study, a two-step prediction methodology was developed to estimate acute aquatic toxicity from molecular structure. In the first step, one-against-the-rest linear discriminant analysis (LDA) models were used to predict the MOA. The LDA models were able to predict the MOA with 85.8-88.8% accuracy for broad and specific MOAs, respectively. In the second step, a multiple linear regression (MLR) model corresponding to the predicted MOA was used to predict the acute aquatic toxicity value. The MOA-based approach was found to yield similar external prediction accuracy (r(2) = 0.529-0.632) to a single global MLR model (r(2) = 0.551-0.562) fit to the entire training set. Overall, the global hierarchical clustering approach yielded a higher combination of accuracy and prediction coverage (r(2) = 0.572, coverage = 99.3%) than the other approaches. Utilizing multiple two-dimensional chemical descriptors in MLR models yielded comparable results to using only the octanol-water partition coefficient (log K(ow)).
Evaluating contaminant sensitivity of threatened and endangered (listed) species and protectiveness of chemical regulations often depends on toxicity data for commonly tested surrogate species. The U.S. EPA's Internet application Web-ICE is a suite of Interspecies Correlation Estimation (ICE) models that can extrapolate species sensitivity to listed taxa using least-squares regressions of the sensitivity of a surrogate species and a predicted taxon (species, genus, or family). Web-ICE was expanded with new models that can predict toxicity to over 250 listed species. A case study was used to assess protectiveness of genus and family model estimates derived from either geometric mean or minimum taxa toxicity values for listed species. Models developed from the most sensitive value for each chemical were generally protective of the most sensitive species within predicted taxa, including listed species, and were more protective than geometric means models. ICE model estimates were compared to HC5 values derived from Species Sensitivity Distributions for the case study chemicals to assess protectiveness of the two approaches. ICE models provide robust toxicity predictions and can generate protective toxicity estimates for assessing contaminant risk to listed species.
Previous modelling of the median lethal dose (oral rat LD) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict these indicators with about 87% accuracy. Toxicity is predicted utilizing the QSAR model fit to chemicals with that indicator. Toxicity was also predicted using a global hierarchical clustering (HC) approach which divides data set into clusters based on molecular similarity. At a comparable prediction coverage (~94%), the global HC method yielded slightly higher prediction accuracy (r = 0.50) than the LDA method (r ~ 0.47). A single model fit to the entire training set yielded the poorest results (r = 0.38), indicating that there is an advantage to clustering the dataset to predict acute toxicity. Finally, this study shows that whilst dividing the training set into subsets (i.e. clusters) improves prediction accuracy, it may not matter which method (expert based or purely machine learning) is used to divide the dataset into subsets.
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