Ecotoxicological QSAR study of fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs): Assessment and priority ranking of the acute toxicity to Pimephales promelas by QSAR and consensus modeling methods

Fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs) have a variety of toxic effects on ecosystems and human body, but the acquisition of their toxicity data is greatly limited by the limited resources available. Here, we followed the EU REACH regulation and used Pimephales promelas as a model organism to investigate the quantitative structure-activity relationship (QSAR) between the FNFPAHs and their toxicity for the aquatic environment for the first time. We developed a single QSAR model (SM1) containing five simple and interpretable 2D molecular descriptors, which met the validation of OECD QSAR-related principles, and analyzed their mechanistic relationships with toxicity in detail. The model had good degree of fitting and robustness, and had better external prediction performance (MAE_test = 0.4219) than ECOSAR model (MAE_test = 0.5614). To further enhance its prediction accuracy, the three qualified single models (SMs) were used for constructing consensus models (CMs), the best one CM2 (MAE_test = 0.3954) had a significantly higher prediction accuracy for test compounds than SM1, and also outperformed the T.E.S.T. consensus model (MAE_test = 0.4233). Subsequently, the toxicity of 252 true external FNFPAHs from Pesticide Properties Database (PPDB) was predicted by SM1, the prediction results showed that 94.84 % compounds were reliably predicted within the model's application domain (AD). We also applied the best CM2 to predict the untested 252 FNFPAHs. Furthermore, we provided a mechanistic analysis and explanation for pesticides ranked as top 10 most toxic FNFPAHs. In summary, all developed QSAR and consensus models can be used as efficient tools for predicting the acute toxicity of unknown FNFPAHs to Pimephales promelas, thus being important for the risk assessment and regulation of FNFPAHs contamination in aquatic environment.

Ecotoxicity of ammonium chlorophenoxyacetate derivatives towards aquatic organisms: Unexpected enhanced toxicity upon oxygen by sulfur replacement

Phenoxyacetate herbicides, such as 2,4-D and MCPA, having a high toxicity to non-target organisms are commonly used for controlling broadleaf weeds in agriculture. However, novel and environmentally friendly analogs are constantly sought after. For this purpose, various substituents at the phenyl group have been tested to find the optimal balance between the potent herbicidal activity and safety for non-target species. In this work, we investigated the influence of the oxygen by sulfur replacement in the phenoxy moiety of ammonium chlorophenoxyacetates on the toxicity towards aquatic organisms, such as bacteria (Vibrio fischeri), water flea (Daphnia magna) and freshwater fish (Pimephales promelas) by determining experimental (Microtox® test - V. fischeri) and predicted (ACD Lab Percepta software - D. magna, P. promelas) EC₅₀/LC₅₀ values. The achieved results showed that in contrary to the literature observations, where O-compounds were more toxic than their S-analogs (urea/thiourea), the O/S replacement in chlorophenoxyacetate significantly increased ecotoxicity of the S-analogs (up to 11 times). Moreover, one- and two-substituted phenoxyacetates in the form of ammonium salts were less toxic to V. fischeri than the commercially available phenoxy herbicides in the acid form. The logP/logD values were also calculated to understand hydro/lipophilic nature of the investigated compounds and differences in their toxicity.

Assessing the fit of biotic ligand model validation data in a risk management decision context

Biotic ligand models (BLMs) have advanced the ability to predict the concentrations of metals in surface waters likely to harm aquatic organisms. BLMs have been developed for several metals including Cu, Zn, Cd, and Ag. Additionally, the US Environmental Protection Agency has published guidance on the use of a BLM to develop water quality criteria for Cu. To validate the predictive performance of many BLMs, model predictions based on test water quality have been compared with corresponding laboratory toxicity measurements. Validation results are typically described in the published literature in terms of the proportion of predicted effect concentrations that fall within a factor of 2 of measured values. In this article, an alternative is presented using a receiver operating characteristics approach and regression prediction limit analyses, quantifying the probabilities of true and false predictions of excess toxicity risk based on toxic unit calculations and a risk management threshold of 1. The approaches are applied to a published Zn BLM and 3 simulated data sets that reflect attributes of other published BLM validation data. The overall accuracy of the unified Zn BLM is estimated to be 80% to 90%, and analyses of simulated data suggest a similar level of accuracy for other published BLMs. Further application of these validation methods to other BLMs may provide more complete and transparent information on their possible predictive value when used in the management of risks due to aqueous metals.