Narcosis and Chemical Reactivity QSARs for Acute Fish Toxicity

Abstract

Quantitative structure activity relationships (QSAR) that describe the acute ®sh toxicity have been published for many different groups of reactive organic chemicals. The structural similarity of chemicals within such groups, suggests that they share a common mode of action (MOA) which is based on their common chemical reactivity. Often, however, a descriptor for this reactivity alone can not explain the observed toxicity satisfactory but addition of a hydrophobicity parameter, like log K OW , is found to improve the relationship. In the present paper, an alternative strategy is proposed and tested with three different literature data sets. Instead of searching for better descriptors to establish a QSAR for the whole data set, the assumption that all compounds within the set act by the same MOA was critically reviewed. We tested the hypothesis that some of the compounds within the data sets acted by narcosis (general anesthesia), a second plausible mode of action in acute ®sh toxicity. Narcosis potency at observed lethal exposure levels was modeled with a baseline toxicity QSAR. The literature data sets were split in a narcosis and a reactive subset and for each of them a separate, one-parameter QSAR was established. For a set of OP-esters, nine out of 20 compounds were identi®ed as possible narcotic compounds and their toxicity could be described with a narcosis QSAR. For the 11 compounds remaining in the reactive subset, a good correlation between acute toxicity and measured, in-vitro AChE inhibition rate was found (r 2 0.68) which would have been overlooked if the whole data set was used. The use of two separate QSARs instead of one mixed QSAR was also tested for literature data sets of nitrobenzenes and a,b-unsaturated carboxylates. It was shown that for the description of toxicity data of all three groups of reactive compounds, a model which uses two separate modes of action was superior to a mixed model which uses a reactivity and a hydrophobicity parameter in a multiple linear regression.

Quantitative structure–activity relationships for the toxicity of organophosphorus and carbamate pesticides to the Rainbow trout Onchorhyncus mykiss

“…An alternative approach to modelling OP and C toxicity in fish is to consider their excess toxicity, T e , which was defined as the ratio between the LC 50 values estimated from a QSAR for baseline (non‐polar) toxicity and the experimental, or observed, LC 50 32. This concept has been applied to assist the classification of chemicals into possible modes or mechanisms of toxic action according to schemes based on threshold values of T e 22, 33. Chemicals with a large T e value (i.e.…”

“…For instance, a threshold value of T e = 5 (i.e. chemicals with an experimental LC 50 value 5 times lower than that predicted by baseline LC 50 ) was selected to classify compounds as non‐polar narcotics 22. According to this value (in its logarithmic form, log T e = 0.7) and baseline toxicity predicted by Eqn (1), the pesticides in the dataset were classified as acting by non‐polar narcosis or being specific toxicants (i.e.…”

“…QSARs have been developed to predict the toxicity and/or AChE inhibition (i.e. the bimolecular rate constant) of mostly OP, but also carbamate, pesticides 7, 19–22. Johnson et al 19 showed that the bimolecular inhibition rate constants for bovine AChE were well correlated with hydrophobic substituent constants and with the presence or absence of cationic groups on the inhibitor, but not with steric substituent constants.…”

“…Verhaar et al 21 applied multiple linear regression and partial least‐squares analyses to predict several biological aspects of OP toxicity in fish. Freidig and Hermens22 differentiated between non‐reactive and reactive OPs on the basis of their ‘excess toxicity’ (toxicity greater than that predicted by narcosis) and built separate models based on log P and the second‐order inhibition rate, log k i , respectively. Lin et al 7 showed that AChE inhibition rate constants of aryl carbamates were related to their Hammett substituent constants.…”

Quantitative Structure–Activity Relationship (QSAR) Modeling of EC 50 of Aquatic Toxicities for Daphnia magna

“…Species such as Vibrio fischeri (Zhao et al, 1998), rainbow trout (Slavov et al, 2008), and fathead minnow (Freidig & Hermens, 2000) are all sensitive to changes in the water environment and have been studied extensively.…”