Interspecies quantitative structure–activity–activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols

Abstract

We propose interspecies quantitative structure-activity-activity relationships (QSAARs), that is, QSARs with descriptors, to estimate species-specific acute aquatic toxicity. Using training datasets consisting of more than 100 aromatic amines and phenols, we found that the descriptors that predicted acute toxicities to fish (Oryzias latipes) and algae were daphnia toxicity, molecular weight (an indicator of molecular size and uptake) and selected indicator variables that discriminated between the absence or presence of various substructures. Molecular weight and the selected indicator variables improved the goodness-of-fit of the fish and algae toxicity prediction models. External validations of the QSAARs proved that algae toxicity could be predicted within 1.0 log unit and revealed structural profiles of outlier chemicals with respect to fish toxicity. In addition, applicability domains based on leverage values provided structural alerts for the predicted fish toxicity of chemicals with more than one hydroxyl or amino group attached to an aromatic ring, but not for fluoroanilines, which were not included in the training dataset. Although these simple QSAARs have limitations, their applicability is defined so clearly that they may be practical for screening chemicals with molecular weights of ≤364.9.

Exploring the role of quantum chemical descriptors in modeling acute toxicity of diverse chemicals to Daphnia magna

“…However, there have been equally important advanced and sophisticated QSAR approaches based on the classification method [13] which can effectively predict whether a chemical is biologically active or inactive. Recently [14][15][16][17][18][19][20][21], such classification based QSAR models have been reported for predicting the toxicity, of large and heterogeneous datasets of compounds, against many organisms, besides assessing multiple toxicological profiles under diverse experimental conditions. For example, Tenorio-Borroto et al [14][15][16], had proposed multi-target quantitative structure-activity/property relationships (mt-QSAR/QSPR) models along with the flow cytometry analysis for the prediction of cytotoxicity and immunotoxicity, which can effectively models the drug-target interactions and effects of organic compounds over the cellular and molecular targets of immune system.…”

“…In a recent study, Kleandrova et al [19] has reviewed significant advancements in the QSAR modeling for the prediction of acute toxicity, and has also introduced a multitasking toxicity model. Besides this, Furuhama et al [20] has proposed quantitative structure-activity-activity relationships (QSAAR) for modeling the species-specific acute aquatic toxicity of aromatic amine and phenols, whereas Speck-Planche et al [21] had predicted multiple ecotoxicological effects of agrochemical fungicides through multi-species QSAR models. Moreover, QSAR approaches based on molecular docking and simulation techniques have also been quite promising [22,23].…”

An Electronic‐structure Informatics Study on the Toxicity of Alkylphenols toTetrahymena pyriformis

“…In the previous publications on prediction of the IGC 50 values of phenol derivatives, different descriptor sets were used, [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] and, as a result, a variety of important descriptors were suggested important to the toxicity. Therefore, it Table 3.…”

“…[11][12][13][14][15][16][17][18][19][20][21][22] This hydrophobicity term, usually given as logP ow represents the ability of a substance to enter into cells through the lipid membranes related to both toxicant uptake and baseline toxicity. Another descriptor frequently suggested in the toxicity analysis is the energy level of the lowest unoccupied molecular orbital (ɛLUMO), [11][12][13] electrophilicity (ω), [23][24] molecular weight (M), [25][26][27] and largest negative or positive charges on oxygen atoms (qO) and hydrogen atoms (qH). [28][29][30] These descriptors have definite physical meanings and are interpretable in the QSAR analysis.…”

Integrated fate assessment of aromatic amines in aerobic sewage treatment plants

“…Aromatic amines are powerful carcinogens, mutagens, and hemotoxicants in humans, animals, and plants (Muz et al 2017a, b;Slavov et al 2018;de los Santos et al 2015). Wastewater contains aromatic amines that have toxic effects on aquatic organisms, including fish, algae, and other aquatic fauna (Burkhardt-Holm et al 1999;Gosetti et al 2010;Muz et al 2017a, b;Furuhama et al 2015). 3,4-Dichloroaniline has been recognized as an endocrine disrupting chemicals (Tasca and Fletcher 2018).…”