Prediction of acute toxicity to Daphnia magna and interspecific correlation: a global QSAR model and a Daphnia-minnow QTTR model

Acute toxicity is an important basis for the assessment of hazardous chemicals, but currently there is a huge data gap in chemical toxicity information. The in silico Quantitative Structure Activity Relationship (QSAR) models can use the existing experimental data information to predict the missing chemical toxicity information data and thus reduce animal testing. In the present study, a global QSAR model for the prediction of acute Daphnia magna toxicity has been developed based on the five principles proposed by the Organization for Economic Co-operation and Development (OECD). Moreover, a Daphnia-minnow (referring specifically to the fathead minnow) Quantitative Toxicity-Toxicity Relationship (QTTR) prediction model has been developed based on the present study and our previous work on fathead minnow (Pimephales promelas). Both the QSAR and QTTR prediction models have good goodness-of-fit, robustness, and predictive ability. Finally, the acute toxicity mode of action (MOA) for fathead minnow and Daphnia magna was compared by toxicity ratio based on interspecies toxicity data. By comparison, Daphnia magna was found more sensitive to anilines and phosphorothioates than fathead minnow. The present models can fill the acute toxicity data gap and contribute to the chemicals risk assessment and priority setting.

Nano-QTTR development for interspecies aquatic toxicity of silver nanoparticles between daphnia and fish

Limited studies of quantitative toxicity-toxicity relationship (QTTR) modeling have been conducted to predict interspecies toxicity of engineered nanomaterials (ENMs) between aquatic test species. A meta-analysis of 66 publications providing acute toxicity data of silver nanoparticles (AgNPs) to daphnia and fish was performed, and the toxicity data, physicochemical properties, and experimental conditions were collected and curated. Based on Euclidean distance (ED) grouping, a meaningful correlation of logarithmic lethal concentrations between daphnia and fish was derived for bare (R²_bare = 0.47) and coated AgNPs (R²_coated = 0.48) when a distance of 10 was applied. The correlation of coated AgNPs was improved (R²_coated = 0.55) by the inclusion of descriptors of the coating materials. The correlations were further improved by R²_bare = 0.57 and R²_coated = 0.81 after additionally considering particle size only, and by R²_bare = 0.59 and R²_coated = 0.92 after considering particle size and zeta potential simultaneously. The developed ED-based nano-QTTR model demonstrated that inclusion of the coating material descriptors and physicochemical properties improved the goodness-of-fit to predict interspecies aquatic toxicity of AgNPs between daphnia and fish. This study provides insight for future in silico research on QTTR model development in ENM toxicology.

Toxicity of contaminants of emerging concern to Dugesia japonica: QSTR modeling and toxicity relationship with Daphnia magna

Freshwater planarian Dugesia japonica has a critical ecological importance owing to its unique properties. This study presents for the first time an in silico approach to determine a priori the acute toxicity of contaminants of emerging concern towards D. japonica. Quantitative structure-toxicity/toxicity-toxicity relationship (QSTR/QTTR) models provided here allow producing reliable information using the existing data, thus, reducing the demand of in vivo and in vitro experiments, and contributing to the need for a more holistic approach to environmental safety assessment. Both models are promising for being notably simple and robust, meeting rigorous validation metrics and the OECD criteria. The QTTR model based on the available Daphnia magna data might also contribute to the US EPA Interspecies Correlation Estimation web application. Moreover, the proposed models were applied on hundreds of environmentally significant chemicals lacking experimental D. japonica toxicity data and predicted toxicity values were reported for the first time. The models presented here can be used as potential tools in toxicity assessment, screening and prioritization of chemicals and development of risk management measures in a scientific and regulatory frame.