Development of QSAAR and QAAR models for predicting fish early-life stage toxicity with a focus on industrial chemicals.

We developed models for predicting fish early-life stage (ELS) toxicities oriented to industrial chemicals. The training set was constructed without data from the Office of Pesticide Programs Pesticide Ecotoxicity Database, the main source for the pesticide-biased training set used in our previous work (SAR QSAR Environ. Res. 29:9, 725-742). In addition to the descriptors from the previous study, we also used water solubility to develop the new models, which were evaluated against the test set used in our previous study so that we could focus on the effects of the different training set and the additional descriptor. The statistics for the new models were hardly better than those for the previous models, which suggests, contrary to our expectations, that pesticide-biased data can successfully be used to develop models for predicting the fish ELS toxicities oriented to industrial chemicals. Acute Daphnia magna toxicity was important for the predictive QSAARs in both studies. A distance-based method for defining the applicability domains indicated that water solubility was a key indicator for detecting underestimated chemicals. The comparison of fish ELS toxicities for chemicals presented in different literatures revealed the uncertainty of the experimental data, which may lead to the low predictivity.

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.

External validation of acute-to-chronic models for estimation of reproductive toxicity to Daphnia magna.

We evaluated the predictivity and applicability of previously proposed models for the reproductive toxicity of chemicals to Daphnia magna [SAR QSAR Environ. Res. 27:10, 833-850] by using external data from the United States Environmental Protection Agency database ECOTOX. These models were based on quantitative structure-activity-activity relationships (QSAARs) and a quantitative activity-activity relationship (QAAR): the models can be categorized as acute-to-chronic models with (QSAAR) and without (QAAR) structural and physicochemical (e.g. distribution coefficients, log D) descriptors. We found that the QSAAR models were suitable for chemicals with an '-NH2 attached to aromatic carbon' sub-structure, whereas the QAAR model was better for multicomponent compounds, coordination complexes, tin compounds and straight-chain primary amines. For chemicals with a known specific mode of action (e.g. pesticides and antibacterial agents and their derivatives), toxicity estimation within the acute-to-chronic framework requires special attention. We evaluated the applicability of the models on the basis of the descriptors in the models. We recommend that chemicals be pre-screened before their toxicities are estimated with these models: pre-screening enabled the estimation of the toxicities of some chemicals within the applicability domains of the models.

Acute to chronic estimation of Daphnia magna toxicity within the QSAAR framework.

We constructed models for acute to chronic estimation of the Daphnia magna reproductive toxicities of chemical substances from their Daphnia magna acute immobilization toxicities. The models combined the acute toxicities with structural and physicochemical descriptors. We used multiregression analysis and selected the descriptors for the models by means of a genetic algorithm. Of the best 100 models (as indicated by the lack of fit score), 90% included the following descriptors: acute toxicity (i.e. an activity parameter), distribution coefficient (log D) and structural indicator variables that indicate the presence of -NH2 attached to aromatic carbon and the presence of a chlorine atom. We compared the predictive abilities of five of these quantitative structure-activity-activity relationship (QSAAR) acute to chronic estimation models with the predictive ability of a simple linear regression model. The comparison revealed that inclusion of structural and physicochemical descriptors such as those in QSAAR models can improve models for extrapolation from acute to chronic toxicity. Our results also provide a QSAAR framework that is expected to be useful for the further development of chronic toxicity estimation models.

Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties.

We propose a three-step strategy that uses structural and physicochemical properties of chemicals to predict their 72 h algal growth inhibition toxicities against Pseudokirchneriella subcapitata. In Step 1, using a log D-based criterion and structural alerts, we produced an interspecies QSAR between algal and acute daphnid toxicities for initial screening of chemicals. In Step 2, we categorized chemicals according to the Verhaar scheme for aquatic toxicity, and we developed QSARs for toxicities of Class 1 (non-polar narcotic) and Class 2 (polar narcotic) chemicals by means of simple regression with a hydrophobicity descriptor and multiple regression with a hydrophobicity descriptor and a quantum chemical descriptor. Using the algal toxicities of the Class 1 chemicals, we proposed a baseline QSAR for calculating their excess toxicities. In Step 3, we used structural profiles to predict toxicity either quantitatively or qualitatively and to assign chemicals to the following categories: Pesticide, Reactive, Toxic, Toxic low and Uncategorized. Although this three-step strategy cannot be used to estimate the algal toxicities of all chemicals, it is useful for chemicals within its domain. The strategy is also applicable as a component of Integrated Approaches to Testing and Assessment.

The dynamics of sexual conflict over mating rate with endosymbiont infection that affects reproductive phenotypes.

Maternally inherited endosymbionts have been implicated as significant drivers of sexual conflict within their hosts, typically through sex-ratio manipulation. Empirical studies show that some of these endosymbionts have the potential to influence sexual conflict not by sex-ratio distortion, but by altering reproductive traits within their hosts. Research has already shown that reproductive traits involved in mating/fertilization process are integral 'players' in sexual conflict, thus suggesting the novel hypothesis that endosymbiont-induced changes in reproductive phenotypes can impact the dynamics of sexual conflict. Here, we use a standard quantitative genetic approach to model the effects of endosymbiont-induced changes in a female reproductive trait on the dynamics of sexual conflict over mating/fertilization rate. Our model shows that an endosymbiont-induced alteration of a host female reproductive trait that affects mating rate can maintain the endosymbiont infection within the host population, and does so in the absence of sex-ratio distortion and cytoplasmic incompatibility.