Quantum chemistry based quantitative structure-activity relationships for modeling the (sub)acute toxicity of substituted mononitrobenzenes in aquatic systems

Abstract

Fifteen experimental literature data sets on the acute toxicity of substituted nitrobenzenes to algae (Scenedesmus obliquus, Chlorella pyrenoidosa, C. vulgaris), daphnids (Daphnia magna, D. carinata), fish (Cyprinus carpio, Poecilia reticulata), protozoa (Tetrahymena pyriformis), bacteria (Phosphobacterium phosphoreum), and yeast (Saccharomyces cerevisiae) were used to establish quantum chemistry based quantitative structure-activity relationships (QSARs). The logarithm of the octanol/water partition coefficient, log Kow, and the energy of the lowest unoccupied molecular orbital, Elumo, were used as descriptors. Suitable QSAR models (0.65 < r2 < 0.98) to predict acute toxicity of substituted mononitrobenzenes to protozoa, fish, daphnids, yeast, and algae have been derived. The log Kow was a sufficient descriptor for all cases, with the additional Elumo descriptor being required only for algae. The QSARs were found to be valid for neutral substituted mononitrobenzenes with no -OH, -COOH, or -CN substituents attached directly to the ring. From the 100,196 European Inventory of Existing Commercial Substances (EINECS), 497 chemicals were identified that fit the selection criteria for the established QSARs. Based on these results, an advisory tool has been developed that directs users to the appropriate QSAR model to apply for various types of organisms within specified log Kow ranges. Using this tool, it is possible to obtain a good indication of the toxicity of a large set of EINECS chemicals and newly developed substituted mononitrobenzenes to five different organisms without the need for additional experimental testing.

Review of (Quantitative) Structure–Activity Relationships for Acute Aquatic Toxicity

“…Examples include models developed for algal toxicity of chlorophenols [17], anilines [18] and benzonitriles [19]; for daphnid toxicity of a-substituted phenylsulphonylacetates [20], quaternary alkylammonium sulphobetains [21], linear alkylbenzene sulphonates and ester sulphonates [22], benzoxazinone allelochemicals [23], for daphnid and fish toxicity of alcohol ethoxilate surfactants [24,25]; for ciliate toxicity of nitrobenzenes [26], alkanol and alkanamines [27], halogenated alkanes, alkanols and alkanitriles [28], lactones [29], phenols [30], and aromatic aldehydes [31], for algae, daphnid, fish, protozoa, bacteria and yeast toxicity of substituted mononitrobenzenes [32], amongst others. There are different motives (scientific, commercial and regulatory) for development of chemical class-based (Q)SARs.…”

“…For example, in defining the domain of the substituted mononitrobenzenes [32], the authors excluded chemicals with cyano-hydroxy-, carboxy-, phenyl-and second nitro-group. Separate models for these classes were not reported and this is probably due to the insufficiency of data for model development.…”

A Machine Learning Approach to Designing Guidelines for Acute Aquatic Toxicity

“…How these properties are relevant to acute toxicity has been described previously [32,[55][56][57][58][59][60][61]. As mentioned in the introduction many of the initial classical QSAR models for acute aquatic toxicity were based on octonal-water coefficient.…”

“…For this pair of features the large differences between the LUMO and HOMO account for a chemicals non toxicity while the PSA can either be small or large. Furthermore, previous studies have shown that compounds with high octonal-water coefficient are more bioaccumulative [57,58]. The aqueous solubility and the polarity of molecule can be mechanistically related to how bioavailable the compounds will be to fish since molecules with high aqueous solubilities and high polarity will be more bioavailable to fish compared to compounds with smaller aqueous solubilities and small polarity.…”

Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines

“…A wide variety of descriptors have been reported to be used in QSAR analysis [7,9,11,[16][17][18]. Recent progress in computational hardware and the development of efficient algorithms have assisted the routine development of molecular quantum chemical calculations.…”