title: QSAR models for predicting the acute toxicity of selected organic chemicals with diverse structures to aquatic non-vertebrates and humans.
abstract: The linear and non-linear relationships of acute toxicity (as determined on five aquatic non-vertebrates and humans) to molecular structure have been investigated on 38 structurally-diverse chemicals. The compounds selected are the organic chemicals from the 50 priority chemicals prescribed by the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme. The models used for the evaluations are the best combination of physico-chemical properties that could be obtained so far for each organism, using the partial least squares projection to latent structures (PLS) regression method and backpropagated neural networks (BPN). Non-linear models, whether derived from PLS regression or backpropagated neural networks, appear to be better than linear models for describing the relationship between acute toxicity and molecular structure. BPN models, in turn, outperform non-linear models obtained from PLS regression. The predictive power of BPN models for the crustacean test species are better than the model for humans (based on human lethal concentration). The physico-chemical properties found to be important to predict both human acute toxicity and the toxicity to aquatic non-vertebrates are the n-octanol water partition coefficient (Pow) and heat of formation (HF). Aside from the two former properties, the contribution of parameters that reflect size and electronic properties of the molecule to the model is also high, but the type of physico-chemical properties differs from one model to another. In all of the best BPN models, some of the principal component analysis (PCA) scores of the 13C-NMR spectrum, with electron withdrawing/accepting capacity (LUMO, HOMO and IP) are molecular size/volume (VDW or MS1) parameters are relevant. The chemical deviating from the QSAR models include non-pesticides as well as some of the pesticides tested. The latter type of chemical fits in a number of the QSAR models. Outliers for one species may be different from those of other test organisms.
authors.affiliation: Laboratory for Biological Research in Aquatic Pollution, University of Ghent, Belgium.
authors.forename: M C
authors.initials: MC
authors.lastname: Calleja
authors.forename: P
authors.initials: P
authors.lastname: Geladi
authors.forename: G
authors.initials: G
authors.lastname: Persoone
mesh_terms: D000826:Animal Testing Alternatives; D000818:Animals; D003603:Cytotoxins / Q000737:chemistry / Q000633:toxicity; D006801:Humans; D006838:Hydrocarbons / Q000737:chemistry / Q000378:metabolism / Q000633:toxicity*; D016018:Least-Squares Analysis; D007928:Lethal Dose 50; D016014:Linear Models; D009682:Magnetic Resonance Spectroscopy; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013056:Spectrophotometry, Ultraviolet; D013329:Structure-Activity Relationship; D018675:Toxicity Tests; D015262:Xenobiotics / Q000737:chemistry / Q000633:toxicity*
chemical_list: D003603:Cytotoxins; D006838:Hydrocarbons; D015262:Xenobiotics