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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, logD) descriptors. We found that the QSAAR models were suitable for chemicals with an ‘–NH2attached 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. [ABSTRACT FROM PUBLISHER]

We constructed models for acute to chronic estimation of theDaphnia magnareproductive toxicities of chemical substances from theirDaphnia magnaacute 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 (logD) and structural indicator variables that indicate the presence of –NH2attached 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. [ABSTRACT FROM AUTHOR]

We propose a three-step strategy that uses structural and physicochemical properties of chemicals to predict their 72 h algal growth inhibition toxicities againstPseudokirchneriella subcapitata. In Step 1, using a logD-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 lowandUncategorized. 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. [ABSTRACT FROM AUTHOR]