Models for risk assessment of reactive chemicals in aquatic toxicology

Abstract

A quantitative structure property relationship (QSPR) for a,b-unsaturated carboxylates (mainly acrylates and methacrylates) was established in chapter 2. Chemical reaction rate constants were measured for 12 different chemicals with three different nucleophiles, namely H 2 O, OH - and glutathione (GSH). Relatively small differences were found in hydrolysis rates (reaction with H 2 O and OH - ). At an elevated pH (8.8) the hydrolysis half-life of the compound ranged between 7 and 40 days, with exception of diethyl fumarate (0.4 day). A separation in two groups was observed for the reaction with GSH (Michael addition), where acrylates reacted approximately 100 times as fast as methacrylates. This difference was con- sistent with differences found in electronic structure, which was determined by quantum- chemical calculations. Because no single parameter could describe the electrophilic charac- ter of the unsaturated carboxylates satisfactory, four descriptors were pooled, using a multivariate correlation (partial least squares regression, PLS). The resulting QSPR for Michael addition was able to predict the reactivity of structurally related, unsaturated carboxylates. Acute fish toxicity of a set of acrylates and methacrylates was evaluated in chapter 3. Published four-day LC 50 data for fathead minnow were compared to the chemical reactivity of the compounds towards GSH, because Michael addition was expected to be the mecha- nism that causes harmful binding to essential biological thiol-sites in the fish (e. g. proteins and enzymes). A simple equation was used to model the interaction of electrophilic chemi- cals with GSH. The degree of GSH depletion, which was used to estimate the toxic effect, was found to be related to the product of aqueous exposure concentration and chemical reaction rate of the reactive compound. Although, all acrylates and methacrylates poten- tially could react with GSH, narcosis was judged to be an alternative mode of toxic action responsible for the observed acute toxicity. Potencies for GSH depletion and narcosis were compared on the bases of critical body residues and critical depletion rates. Five out of 12 compounds were thereby identified as narcosis chemicals on the bases of their high calcu- lated lethal body burden. It was concluded that, although the tested chemicals all contained a similar functional group, their mode of action regarding acute fish toxicity was not the same. Therefore, a correlation between chemical reaction rate and LC 50 for the whole test set of chemicals would not be meaningful. The results from chapter 3 indicated, that narcosis was an interfering mode of action in QSARÕs for fish toxicity of reactive chemicals. To evaluate this hypothesis, data of reactive chemicals from three different classes (unsaturated carboxylates, organophosphorus esters?126 Chapter 8 Summary and General Discussion and nitrobenzenes) were taken from the literature and subjected to an analysis for multiple modes of action (chapter 4). The Toxic ratio, being the ratio between the observed LC 50 and the LC 50 , predicted for the same compound by a narcosis QSAR was used to estimate the probability of a compound to act by narcosis. In total, 40 % of the 61 compounds tested were identified as Òprobably acting by narcosisÓ. For these compounds, a narcosis QSAR using the octanol/water partitioning coefficient (K OW ) as sole descriptor was found to describe the toxicity. QSARÕs using reactivity descriptors, which in earlier work had been found insufficient to describe the toxicity of these classes of compounds improved considerably, if the Ònarcotic chemicalsÓ were excluded from the data sets. It was concluded, that narcosis should always be considered as a possible alternative cause of death in acute fish toxicity test, even if the chemicals seem to have a very specific mode of action. Additionally, it was shown, that QSARÕs should only be established for sets of chemicals with an identical mode of action. Modes of action clearly should not be confused with functional groups. The toxic effect of acrylates and methacrylates on a cellular level were investigated in chapter 5. Cellular glutathione (GSH) concentrations were recorded in isolated cells of rat livers. These cells have a continuous high expression of GSH and a broad range of metabo- lism. Potentially toxic metabolites of the acrylates and methacrylates were therefore likely to be produced in these cells. Furthermore, the additivity of the toxic effect of these chemi- cals was investigated in this in-vitro test. For each chemical, an EC 50 for GSH depletion was determined and used as an effect equivalent to compare their potencies. By testing two mixtures, each containing six individual chemicals, it could be shown that the depletion of GSH was dose-additive. This means that in a mixture of acrylates and methacrylates each individual chemical will contribute to the total toxic effect of the mixture. As expected, the compounds were metabolized by the hepatocytes. For one of them, allyl methacrylate, the very toxic metabolic product acrolein could be identified in the cell-culture medium. The production of this metabolite is most probably responsible for the high toxicity of this spe- cific compound towards the liver cells as well as towards fish (chapter 3). A preliminary physiologically based pharmacokinetic and -dynamic model (PBPK-PD) for ethyl acrylate (EA) was presented in chapter 6. It was based on an existing PBPK model for inert compounds in fish, which had been established by the US-EPA in Duluth, MN ( 1, 2). The model was adapted to be used with EA by adding elimination processes in several tissue compartments. Elimination rates of EA, which had been measured in-vitro, were extrapolated to whole organs. The turnover of GSH in the gills was modeled separately and was used to describe the toxic effect of EA on biological targets. Once the model was estab-?lished, several aspects of an aqueous exposure scenario were investigated. The uptake of EA in different organs of the fish was predicted to occur very rapidly (steady state concen- trations reached in minutes to a few hours) with exception of the fat tissue. The metabolic elimination of EA in the gills was not sufficient to cause a notable first pass effect. Conse- quently, the EA concentration in the gill tissue was predicted to be almost instantaneously at equilibrium with the aqueous exposure concentration. The EA concentration in the gills was subsequently used in the biological effect sub-model to describe the depletion of GSH. For a simulated exposure scenario close to a lethal aqueous concentration, the GSH concen- tration in the gills decreased by 60 % during the first 6 hours. This forecast was in agree- ment with experimental observations. In contrast to an existing rat model for EA, the trout model did not predict a first pass elimination of EA and therefore a systemic distribution can be expected in the fish. In both models, however, a local depletion of the GSH level at the site of adsorption was evident. In chapter 7, several findings from the previous chapters were combined to postulate an elementary approach to model toxic effects of reactive chemicals in aquatic organisms. The most important simplification of this approach was, to disregard the pharmacokinetics of moderately hydrophobic reactive chemical in aquatic organisms. This resulted in a elemen- tary pharmacodynamic model (EPD), which describes a target and the interaction of a reac- tive chemical with this target. This approach can be used to describe time and concentration dependent toxicological effects. Models, based on this approach were found to give excel- lent description of experimental data on acetylcholine-esterase inhibition due to OP-esters in several aquatic animals. The approach was also able to predict time dependent effect concentrations (e. g. LC 50 ). Under certain conditions, the EPD model can be reduced to an equivalent of HaberÕs Law, which states that the product of concentration and exposure time will be constant. In addition to this, the EPD model can give a rational interpretation of threshold concentration, which are often observed in toxicity experiments.