QSAR-based toxicity classification and prediction for single and mixed aromatic compounds

Quantitative structure-activity relationships (QSARs) based on the octanol/water partition coefficient were employed to predict acute toxicities of 36 substituted aromatic compounds and their mixtures. In this study, the model developed by Verhaar et al. was modified and used to calculate octanol/water partition coefficients of chemical mixtures. To validate the model, acute toxicities of these chemicals were measured to Vibrio fischeri in terms of EC⁵⁰. The results indicated that the obtained QSAR models could be used to predict toxicities of samples consisting of these substituted aromatic compounds, individually or in combinations. The obtained equations were proved to be robust enough by using the leave-one-out test method. By classifying these chemicals into two groups, polar and non-polar, the toxicities of chemical mixtures within each group can be predicted accurately from their calculated partition coefficients.     

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.     

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and K OW for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.     

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.     

Total surface areas of Group IVA organometallic compounds: Predictors of toxicity to algae and bacteria

There is a high correlation between molecular surface area (TSA) of triorganotin and triorganolead compounds and their toxicity towards a bacterium (Escherichia coli) and an alga (Selenastrum capricornutum). Parallel attempts to correlate other Group IVA organometals incorporating silicon or germanium were unsuccessful. It was further demonstrated, however, that a high correlation was obtainable between certain series of compounds with the same organic substituent but different metal centers involving all Group IVA elements. In both instances, the inability to obtain a quantitative structure-activity relationship (QSAR) for all systems studied appears to be a function of the solubility of the compounds. While organotin TSA values have been found to correlate well with their toxicities toward various organisms, this study clearly suggests that this type of QSAR can be readily extended to include other organometal systems, provided that there is no solubility problem and the toxicity is a function of the hydrophobicity of the organometal compounds.     

A Noncongeneric Model for Predicting Toxicity of Organic Molecules to Vibrio Fischeri

Abstract

A general Quantitative Structure-Activity Relationship (QSAR) model on Vibrio fischeri (Microtox^? test) was derived using the autocorrelation method for describing the molecules and a neural network as statistical tool. From a training set of 1068 organic chemicals described by means of four different autocorrelation vectors, it was possible to obtain valuable models but presenting some large outliers. Addition of the time of exposure as variable allowed us to derive a more powerful model from 2795 toxicity results. The predictive power of this 36/26/1 neural network model was tested on an external testing set of 385 toxicity data and compared with the performances of linear models designed for polar narcotic amines and for weak acid respiratory uncouplers.     

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and Kow for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.