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.     

QSAR Modeling of Large Heterogeneous Sets of Molecules

Abstract

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large non-congeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.     

Computational Chemistry in Environmental Toxicology QSAR

Abstract

Computational chemistry provides a means for the calculation or estimation of three-dimensional chemical structure, organization and analysis of chemical data, classification of industrial chemicals by structure and properties, prediction of toxicity, and identification of chemical structure. The development of the EPA National Environmental Supercomputer Center (NESC) in Bay City, Michigan, makes available to scientists in EPA Headquarters, the ability to perform advanced QSAR modeling. This provides the means to develop and apply QSAR models for chemicals acting by a variety of molecular mechanisms. The work makes possible improved programmatic support to the Office of Pollution Prevention and Toxics under the Toxic Substances Control Act and the Pollution Prevention Act.     

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.     

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.     

Joint QSAR Analysis Using the Free-Wilson Approach and Quantum Chemical Parameters

Abstract

A new quantitative structure-activity relationship (QSAR) technique combining the Free-Wilson method and constructed quantum chemical parameters was used to simulate the aqueous solubility (S _w), 1-octanol/water partition coefficient (K _ow) of 14 new synthesized benzanilide derivatives and their 96 h acute toxicity (EC₅₀) to Daphnia magna. The mode of action of the 14 selected compounds to Daphnia magna was shown to be a complex process involving a physical partition stage and a biochemical reaction stage. The results also indicated that the joint (QSAR) analysis was much effective than the original Free-Wilson method and Hansch method not only in predicting properties/toxicity, but also in investigating the mode of action of chemicals.     

SAR—The U.S. Regulatory Perspective

Abstract

Structure-Activity Relationships (SAR) have been used for over a decade by the U.S. EPA's Office of Pollution Prevention and Toxics (OPPT) in their new chemicals program. The development and use of SAR resulted from the need to make rapid risk-based decisions on thousands of new chemicals per year while seldom receiving data on chemical properties, potential exposures, or hazards to humans or organisms in the environment. Qualitative SAR and quantitative SAR methods (QSAR) have been used to fill some of these data gaps by estimating the potential properties and hazards of such chemicals. SAR has been used to assess chemical hazards, identify testing needs, and set priorities. Validation of these SAR assessment tools is an ongoing process.     

Use of Quantitative Structure Activity Relationship Methods in Predicting Activated Carbon Adsorption Isotherms for Hazardous Air Pollutants

Abstract

A quantitative structure activity relationship (QSAR) approach is presented to predict gas phase activated carbon adsorption capacities and isotherms for several organic chemicals and their binary mixtures. F,x peri menial adsorption data reported in the literature for various binary mixtures on two di ercnt carbons were used to validate this predictive approach. The QSAR-predicted and experimental adsorption capacities for di erent chemical mixtures of small apolar hydrocarbons at di erent temperatures on two di erent carbons under a range of total pressures and gas phase compositions agreed well with r₂ = 0.85 for a total of 338 data points.     

U.S. EPA Regulatory Perspectives on the Use of QSAR for New and Existing Chemical Evaluations

Abstract

As testing is not required, ecotoxicity or fate data are available for ≈ 5% of the approximately 2,300 new chemicals/year (26,000 + total) submitted to the US-EPA. The EPA's Office of Pollution Prevention and Toxics (OPPT) regulatory program was forced to develop and rely upon QSARs to estimate the ecotoxicity and fate of most of the new chemicals evaluated for hazard and risk assessment. QSAR methods routinely result in ecotoxicity estimations of acute and chronic toxicity to fish, aquatic invertebrates, and algae, and in fate estimations of physical/chemical properties, degradation, and bioconcentration. The EPA's Toxic Substances Control Act (TSCA) Inventory of existing chemicals currently lists over 72,000 chemicals. Most existing chemicals also appear to have little or no ecotoxicity or fate data available and the OPPT new chemical QSAR methods now provide predictions and cross-checks of test data for the regulation of existing chemicals. Examples include the Toxics Release Inventory (TRI), the Design for the Environment (DfE), and the OECD/SIDS/HPV Programs. QSAR screening of the TSCA Inventory has prioritized thousands of existing chemicals for possible regulatory testing of: 1) persistent bioaccumulative chemicals, and 2) the high ecotoxicity of specific discrete organic chemicals.     

Thermal and volumetric properties of methanol–hexamethylphosphortriamide mixtures under standard conditions

Enthalpic and volumetric characteristics of mixing in a methanol (MeOH)–hexamethylphosphortriamide (HMPT, 2) mixture are studied. Based on an analysis of concentration changes in the obtained data and the calculated partial molar characteristics, it is shown that at 0.2 molar fractions > х ₂ > 0.7 molar fractions, the variation in the composition of the mixture slightly alters the character of intermolecular interactions characteristic of pure components. It is found that MeOH–HMPT mixtures experience most changes in intermolecular interaction and structure within the range of 0.2–0.7 molar fractions of HMPT.

     

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 octano/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 consi sting 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.     

Joint QSAR analysis using the Free-Wilson approach and quantum chemical parameters

A new quantitative structure-activity relationship (QSAR) technique combining the Free-Wilson method and constructed quantum chemical parameters was used to simulate the aqueous solubility (Sw), 1-octanol/water partition coefficient (Kow) of 14 new synthesized benzanilide derivatives and their 96 h acute toxicity (EC50) to Daphnia magna. The mode of action of the 14 selected compounds to Daphnia magna was shown to be a complex process involving a physical partition stage and a bio-chemical reaction stage. The results also indicated that the joint (QSAR) analysis was much effective than the original Free-Wilson method and Hansch method not only in predicting properties/toxicity, but also in investigating the mode of action of chemicals.     

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.