Prediction of acute toxicity to Daphnia magna and interspecific correlation: a global QSAR model and a Daphnia-minnow QTTR model. Issue 8 (3rd August 2022)

• Record Type: Journal Article
• Authors: Wu, X.; Guo, J.; Dang, G.; Sui, X.; Zhang, Q.
• Is Part Of: SAR and QSAR in environmental research. Volume 33:Issue 8(2022)
• Page Start: 583
• Page End: 600
• Publication Date: 2022-08-03
• Subjects: QSAR -- QTTR -- interspecific correlation -- genetic algorithm -- mode of action -- toxicity ratio; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2022.2098814
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: 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.