Ecotoxicological prediction of organic chemicals toward Pseudokirchneriella subcapitata by Monte Carlo approach

Abstract

In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria. Green alga Pseudokirchneriella subcapitata has been used for ecotoxicological studies to assess the toxicity of different toxic chemicals in freshwater. Quantitative Structure Activity Relationships (QSAR) are mathematical models to relate chemical structure and activity/physicochemical properties of chemicals quantitatively. Herein, Quantitative Structure Toxicity Relationship (QSTR) modeling is applied to assess the toxicity of a data set of 334 different chemicals on Pseudokirchneriella subcapitata, in terms of EC₁₀ and EC₅₀ values. The QSTR models are established using CORAL software by utilizing the target function (TF₂) with the index of ideality of correlation (IIC). A hybrid optimal descriptor computed from SMILES and molecular hydrogen-suppressed graphs (HSG) is employed to construct QSTR models. The results of various statistical parameters of the QSTR model developed for pEC₁₀ and pEC₅₀ range from excellent to good and are in line with the standard parameters. The models prepared with IIC for Split 3 are chosen as the best model for both endpoints (pEC₁₀ and pEC₅₀). The numerical value of the determination coefficient of the validation set of split 3 for the endpoint pEC₁₀ is 0.7849 and for the endpoint pEC₅₀, it is 0.8150. The structural fractions accountable for the toxicity of chemicals are also extracted. The hydrophilic attributes like 1…n…(… and S…(…… exert positive contributions to controlling the aquatic toxicity and reducing algal toxicity, whereas attributes such as c…c…c…, C…C…C… enhance lipophilicity of the molecules and consequently enhance algal toxicity.