Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system,March 2009 version

The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure–activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r ² > 0.7, RMSE ≤ 0.5, and n > 5, give acceptable q ² values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root mean square error (RMSE). These findings demonstrate that the KATE system has the potential to enable chemicals to be categorised as potential hazards.     

Structure-Toxicity Relationships for Aminoalkanols: A Comparison with Alkanols and Alkanamines

Abstract

The relative toxicity (logIGC^?1 ₅₀) of 49 selected aliphatic amines and aminoalkanols was evaluated in the static Tetrahymena pyriformis population growth impairment assay. Excess toxicity, indicated by potency greater than predicted for non-polar narcotic alkanols, was associated with both classes of test chemicals. Moreover, the aminoalkanols were found to be more toxic than the corresponding alkanamines. A high quality 1-octanol/water partition coefficient (log K _ow) dependent quantitative structure-activity relationship (QSAR), logIGC^?1 ₅₀ = 0.78 (log K _ow)-1.42; r ² = 0.934, was developed for alkanamines. This QSAR represented the amine narcosis mechanism of toxic action. No quality QSAR was developed for the aminoalkanols. However, several structure-toxicity features were observed for this class of chemicals. Two-amino-1-hydroxy derivatives being more toxic than the corresponding derivatives, where the amino and hydroxy moieties were separated by methylene groups. Hydrocarbon branching next to the amino moiety resulted in decreased toxicity. Aminoalkanol alters lipid metabolism in T. pyriformis.     

Application of Hydrophobicity Parameters to Prediction of the Acute Aquatic Toxicity of Commercial Surfactant Mixtures

Abstract

Ethoxylated alcohols are the most extensively used nonionic surfactants in detergent products. The application of QSAR to their aquatic toxicity is complicated by the fact that they are multicomponent mixtures, the parent alcohols being often mixtures of isomers and homologues, each one being ethoxylated to varying degrees. A spreadsheet method for calculation of aquatic toxicity of such nonionic surfactant mixtures is presented. The method is based on a combination of the Könemann narcosis QSAR and mixture toxicity equations based on the principle of concentration addition. Log P values used in the spreadsheet calculations are themselves calculated by spreadsheet formulae based on the Leo and Hansch method modified by incorporation of the position dependent branching factor originally applied to linear alkylbenzene sulphonates. Close agreement between calculated and experimental EC50 values (48 hr Daphnia tests) is obtained for a range of ethoxylated alcohols having a diversity of branching patterns, carbon numbers and degrees of ethoxylation. The effects of increasing carbon number (decreasing EC50), branching (increasing EC50) and increasing degree of ethoxylation (increasing EC50) are all quantified.     

Assessment of in silico models for acute aquatic toxicity towards fish under REACH regulation

We evaluated the performance of eight QSAR in silico modelling packages (ACD/ToxSuite?, ADMET Predictor?, DEMETRA, ECOSAR, TerraQSAR?, Toxicity Estimation Software Tool, TOPKAT? and VEGA) for acute aquatic toxicity towards two species of fish: Fathead Minnow and Rainbow Trout. For the Fathead Minnow, we compared model predictions for 567 substances with the corresponding experimental values for 96-h median lethal concentrations (LC50). Some models gave good results, with r² up to 0.85. We also classified the predictions of all the models into four toxicity classes defined by CLP. This permitted us to assess other parameters, such as the percentage of correct predictions for each class. Then we used a set of 351 substances with toxicity data towards Rainbow Trout (96-h LC50). In this case the predictability was unacceptable for all the in silico models. The calculated r² gave poor correlations (≤0.53). Another analysis was performed according to chemical classes and for mode of action. In the first case, all the classes show a high percentage of correct predictions, in the second case only narcotics and polar narcotics were predicted with good confidence. The results indicate the possibility of using in silico methods to estimate aquatic toxicity within REACH regulation, after careful evaluation.     

Prediction of acute aquatic toxicity in Tetrahymena pyriformis – ‘Eco-Derek’, a knowledge-based system approach£

A ‘proof-of-concept’ version of a software tool for making transparent predictions of acute aquatic toxicity has been developed. It is primarily limited to semi-quantitative predictions in one species, the ciliated protozoan, Tetrahymena pyriformis. A freely available system, ‘Eco-Derek’, was derived by adapting a well-established, knowledge-based structure–activity and reasoning platform (Derek for Windows, Lhasa Limited). The Derek reasoning code was modified to express potency rather than confidence. Structure–activity relationship (SAR) development utilised a curated version of a published dataset, supplemented with the CADASTER Challenge datasets. Forty-five structural alerts were produced. The dependence on log P was examined for each alert and entered into the system as qualitative reasoning rules specifying the predicted potency as Very Low, Low, Moderate, High or Very High. Evaluation studies showed: (a) moderate accuracy for the training set but low accuracy for an external test set; (b) non-linearity in the toxicity–log P relationship for chemicals without identified structural alerts; (c) insufficient differentiation of substituent effects in some of the reactivity-based structural alerts resulting in too few chemicals predicted with Very High toxicity; and (d) the need for additional structural alerts covering polar narcosis and less common reactive or metabolically activated chemical functionality.     

Identification of Phototoxic Polycyclic Aromatic Hydrocarbons in Sediments Through Sample Fractionation and QSAR Analysis

Abstract

The toxicity of certain polycyclic aromatic hydrocarbons (PAHs) can be greatly increased by simultaneous exposure of test organisms to ultraviolet (UV) wavelengths present in sunlight. This phenomenon, commonly termed photoinduced toxicity, had been evaluated extensively in laboratory settings where only one chemical of concern was present. However, more recent studies have demonstrated that complex mixtures of PAHs present, for example in sediments, also can cause phototoxicity to a variety of aquatic species when the samples are tested in simulated sunlight. Unfortunately, because these types of samples can contain thousands of substituted and unsubstituted PAHs it is difficult, if not impossible, to use conventional analytical techniques to identify those responsible for photoinduced toxicity. The objective of the present study was to link two powerful ecotoxicology tools, toxicity-based fractionation techniques and QSAR models, to identify phototoxic chemicals in a sediment contaminated with PAHs emanating from an oil refinery. Extensive chromatographic fractionation of pore water from the sediment, in conjunction with toxicity testing, yielded a simplified set of sample fractions containing 12 PAHs that were identified via mass spectroscopy. Evaluation of these compounds using a recently developed QSAR model revealed that, based upon their HOMO-LUMO gap energies, about half were capable of producing photoinduced toxicity. We further evaluated the phototoxic potential of the reduced set of PAHs by determining their propensity to bioaccumulate in test organisms, through calculation of octanol-water partition coefficients for the chemicals. These studies represent a novel linkage of sample fractionation methods with QSAR models for conducting an ecological risk assessment.     

Quantitative Relationships of Structure-Activity and Volume Fraction For Selected Nonpolar and Polar Narcotic Chemicals

Abstract

The relative toxicity of selected industrial organic chemicals was secured from the literature for the static 48-h Tetrahymena pyriformis 50% population growth impairment and the flow-through 96-h Pimephales promelas 50% mortality endpoints. Chemicals were selected to represent the nonpolar narcosis (aliphatic alcohols and aliphatic ketones) and polar narcosis (anilines and phenols) mechanisms of toxic action. molar volume (MV) and 1-octanol/water partition coefficient (log K _ow) data were generated for each chemical. High-quality, log K _ow dependent quantitative structure-activity relationships were observed for each chemical class and mechanism of action for both endpoints. The volume fraction (V _t) for each chemical in the target phase was determined from the toxicant concentration in the water (toxicity data), the MV, and the target/water partition coefficient (K _tw) with K _tw considered equal to K _ow ^(1-a). Analyses of target sites, by way of “a” revealed that “a” was constant for a mechanism of action regardless of chemical class, but distinct for a given test system. Mean V _t was constant for each mechanism of action regardless of chemical class or test system. These results suggest, at least for reversible physical mechanisms, that volume fraction analyses are significant in determining the mechanism of toxic action of a chemical.