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ABSTRACT
Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors.
We investigated a large acute toxicity data set of five aquatic organisms, fish, Daphnia magna, and algae from the VEGA-Hub, as well as Tetrahymena pyriformis and Vibrio fischeri.
The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.
Acknowledgements
We thank Prof. Paola Gramatica for the free license of QSARINS and Dr. Antreas Afantitis (NovaMechanics Ltd) for providing Enalos+ KNIME nodes. We gratefully acknowledge the contribution of the LIFE project VERMEER (LIFE16/ENV/IT/000167). K. Bouhedjar thanks the Algerian Ministry of Higher Education and Scientific Research for the E.N.P (Exceptional National Program) 2019/2020 fellowship.
Disclosure statement
No potential conflict of interest was reported by the authors.
Supplementary material
Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2020.1810770.