Quantitative Structure-Activity Relationships of Environmental Pollutants

Marine Algal Sensitivity to Source and Weathered Oils

After the Deepwater Horizon oil spill in 2010, toxicity tests were conducted using 4 microalgae (Dunaliella tertiolecta, Skeletonema costatum, Isochrysis galbana, and Thalassiosira pseudonana) and one macroalga (Ectocarpus siliculosus) to study potential impacts on phytoplankton and other primary producers in the Gulf of Mexico and characterize species sensitivity. Tests were performed with Corexit 9500 and fresh source oil and weathered oil samples collected from the field during the Deepwater Horizon oil spill. Because crude oils are mixtures of poorly water-soluble hydrocarbons, dosing was performed using water-accommodated fractions (WAFs) and chemically enhanced (CE) WAFs with the addition of dispersant at a 1:20 dispersant:oil ratio using standard toxicity testing protocols. Exposure media were analyzed for volatile organic compounds, parent and alkylated polycyclic aromatic hydrocarbons, and saturated hydrocarbon compounds. Toxicity was reported as no-observable effect concentration and median effect concentration (EC50) values for average specific growth rate based on nominal percent dilution of stock solution WAFs and sum of dissolved oil toxic units for WAF/CEWAF tests. The macroalga and green alga D. tertiolecta were largely unaffected by any WAF or CEWAFs tested. Isochrysis galbana was found to be the most sensitive species overall with significant growth rate inhibitions for dispersant and all the WAFs/CEWAFs tested. Physically dispersed source oils were generally more toxic than weathered oils. The protectiveness of the chronic toxic units was effective at identifying observed algal growth rate inhibitions across algal species and oil types despite the impact of dispersants.  Environ Toxicol Chem 2021;40:2742-2754. © 2021 SETAC.

Phototoxic target lipid model of single polycyclic aromatic hydrocarbons

A phototoxic target lipid model (PTLM) is developed to predict phototoxicity of individual polycyclic aromatic hydrocarbons (PAHs) measured either as median lethal concentration (LC50) or median lethal time (LT50) for a 50% toxic response. The model is able to account for the differences in the physical/chemical properties of PAHs, test species sensitivities, and variations in light source characteristics, intensity, and length of exposure. The PTLM is based on the narcotic target lipid model (NTLM) of PAHs. Both models rely on the assumption that mortality occurs when the toxicant concentration in the target lipid of the organism reaches a threshold concentration. The PTLM is applied to observed LC50s and LT50s for 20 individual PAHs, 15 test species-including arthropods, fishes, amphibians, annelids, mollusks, and algae-exposed to simulated solar and various UV light sources, for exposure times varying from less than 1 h to 100 h, a total of 333 observations. The LC50 concentrations range from less than 0.1 µg/L to greater that 10⁴  µg/L. The model has 2 fitting parameters that are constant and apply to all PAHs and organisms. The root mean square errors of prediction for log(LC50) and log(LT50) are 0.473 and 0.382, respectively. The results indicate that the PTLM can predict the phototoxicity of single PAHs over a wide range of exposure conditions and to organisms with a wide range of sensitivities. Environ Toxicol Chem 2017;36:926-937. © 2016 SETAC.

The Integrated Use of Alternative Methods in Toxicological Risk Evaluation - ECVAM Integrated Testing Strategies Task Force Report 1

The ECVAM Task Force on Integrated Testing Strategies was established in December 1996, with the remit of assessing the current status of integrated toxicity testing, and of making proposals regarding the design and implementation of integrated testing strategies. The first step in an integrated testing strategy is usually to determine the chemical functionality of a substance, on the basis of its structure and physicochemical properties. The biokinetic and dynamic behaviours of the chemical in various in vitro systems are then assessed. The various elements are then integrated, in either a parallel or a stepwise fashion, to make predictions of the local or systemic toxicity of the chemical of interest. In this report, a generic scheme for local/systemic toxicity, and a specific scheme for target organ toxicity, are proposed. The scope and limitations of the approaches are discussed. The task force hopes that its proposals will stimulate a discussion on the feasibility of this type of approach and it welcomes any feedback. It is planned that the discussion points will be elaborated in a second task force report.

Physiological modes of action of fluoxetine and its human metabolites in algae

Fluoxetine, the active ingredient of many antidepressants, was identified as specifically toxic toward algae in a quantitative structure-activity relationship (QSAR) analysis with literature data for algae, daphnia, and fish. The goal of this study was to elucidate the mode of action in algae and to evaluate the toxicity of the major human metabolites of fluoxetine using two different algae tests. The time dependence and sensitivity of thedifferenteffectendpointsyield information on the physiological mode of action. Baseline toxicity was predicted with QSARs based on measured liposome-water partition coefficients. The ratio of predicted baseline toxicity to experimental toxicity (toxic ratio TR) gives information on the intrinsic potency (extent of specificity of effect). The metabolite p-trifluoromethylphenol was classified to act as baseline toxicant Fluoxetine (TR 60-150) and its pharmacologically active metabolite norfluoxetine (TR 10-80) exhibited specific toxicity. By comparison with reference compounds we conclude that fluoxetine and norfluoxetine have an effect on the energy budget of algal cells since the time pattern of these two compounds is most similar to that observed for norflurazon, but they act less specifically as indicated by lower TR values and the similarity of the effect pattern to baseline toxicants. The mixture toxicity of fluoxetine and its human metabolites norfluoxetine and p-TFMP can be predicted using the model of concentration addition for practical purposes of risk assessment despite small deviations from this model for the specific endpoints like PSII inhibition because the integrative endpoints like growth rate and reproduction in all cases gave agreement with the predictions for concentration addition.

Toxicity of substituted anilines to Pseudokirchneriella subcapitata and quantitative structure-activity relationship analysis for polar narcotics

This study evaluated the toxic effects of substituted anilines on Pseudokirchneriella subcapitata with the use of a closed algal toxicity testing technique with no headspace. Two response endpoints (i.e., dissolved oxygen production [DO] and algal growth rate) were used to evaluate the toxicity of anilines. Both DO and growth rate endpoints revealed similar sensitivity to the effects of anilines. However, trichloroanilines showed stronger inhibitory effects on microalgal photosynthetic reactions than that on algal growth. For various aquatic organisms, the relative sensitivity relationship for anilines is Daphnia magna > luminescent bacteria (Microtox) > or = Pocelia reticulata > or = Pseudokirchneriella subcapitata > or = fathead minnow > Tetrahymena pyriformis. The susceptibility of P. subcapitata to anilines is similar to fish, but P. subcapitata is apparently less sensitive than the water flea. The lack of correlation between the toxicity revealed by different aquatic organisms (microalgae, D. magna, luminescent bacteria, and P. reticulata) suggests that anilines might have different metabolic routes in these organisms. Both hydrogen bonding donor capacity (the lowest unoccupied molecular orbital energy, Elumo) and hydrophobicity (1-octanol:water partition coefficient, Kow) were found to provide satisfactory descriptions for the toxicity of polar narcotics (substituted anilines and chlorophenols). Quantitative structure-activity relationships (QSARs) based on Elumo, log Kow, or both values were established with r2 values varying from 0.75 to 0.92. The predictive power for the QSAR models were found to be satisfactory through leave-one-out cross-validation. Such relationships could provide useful information for the estimation of toxicity for other polar narcotic compounds.

Megavariate Analysis of Environmental QSAR Data. Part II – Investigating Very Complex Problem Formulations Using Hierarchical, Non-Linear and Batch-Wise Extensions of PCA and PLS

Three extensions of the basic PCA and PLS methodologies are described. These extensions are hierarchical, non-linear and batch-based in nature. The objectives of these methods are to assist in problem understanding and problem solving in very complex (QSAR) problem formulations. The method extensions are illustrated using two example QSAR data sets containing many X- and Y-variables.

Carcinogenicity of the aromatic amines: from structure-activity relationships to mechanisms of action and risk assessment

Aromatic amines represent one of the most important classes of industrial and environmental chemicals: many of them have been reported to be powerful carcinogens and mutagens, and/or hemotoxicants. Their toxicity has been studied also with quantitative structure-activity relationship (QSAR) methods: these studies are potentially suitable for investigating mechanisms of action and for estimating the toxicity of compounds lacking experimental determinations. In this paper, we first summarized the QSAR models for the rodent carcinogenicity of the aromatic amines. The gradation of potency of the carcinogenic amines depended firstly on their hydrophobicity, and secondly on electronic (reactivity, propensity to be metabolically transformed) and steric properties. On the contrary, the difference between carcinogenic and non-carcinogenic aromatic amines depended mainly on electronic and steric properties. These QSARs can be used directly for estimating the carcinogenicity of aromatic amines. A two-step prediction is possible: (1) estimation of yes/no activity; (2) if the answer from step 1 is yes, then prediction of the degree of potency. The QSARs for rodent carcinogenicity were put in a wider context by comparing them with those for: (a) Salmonella mutagenicity; (b) general toxicity; (c) enzymatic reactions; (d) physical-chemical reactions. This comparative QSAR exercise generated a coherent global picture of the action mechanisms of the aromatic amines. The QSARs for carcinogenicity were similar to those for Salmonella mutagenicity, thus pointing to a similar mechanism of action. On the contrary, the general toxicity QSARs (both in vitro and in vivo systems) were mostly based on hydrophobicity, pointing to an aspecific mechanism of action much simpler than that for carcinogenicity and mutagenicity. The oxidation of the amines (first step in the main metabolic pathway leading to carcinogenic and mutagenic species) had identical QSARs in both enzymatic and physical-chemical systems, thus providing evidence for the link between simple chemical reactions and those in biological systems. The results show that it is possible to generate mechanistically and statistically sound QSAR models for rodent carcinogenicity, and indirectly that the rodent bioassay is a reliable source of good quality data.

Prediction of the Daphnia acute toxicity from heterogeneous data

Two descriptors (log(P(ow)), 'hardness') were selected to predict the Daphnia acute toxicity of a training set of heterogeneous chemical compounds. The data were extracted from 523 notification files about new chemicals stored at the French Department of Environment. The selection of the descriptors was carried out using a statistical method coupling ordinary least square (OLS) regression and genetic algorithm (GA). The validity limits for the final equation are discussed by comparing the actual and predicted activities of several compounds. The study points out the interest of the 'hardness' parameter for quantitative structure-activity relationships (QSAR) with a heterogeneous data set.

Degradation of atrazine, metolachlor, and pendimethalin in pesticide-contaminated soils: effects of aged residues on soil respiration and plant survival

This study was conducted to determine the effects of pesticide mixtures on degradation patterns of parent compounds as well as effects on soil microbial respiration. Bioavailability of residues to sensitive plant species was also determined. Soil for this study was obtained from a pesticide-contaminated area within an agrochemical dealer site. Degradation patterns were not affected by the presence or absence of other herbicides in this study. Atrazine concentrations were significantly lower at 21 through 160 days aging time compared to day 0 concentrations. Metolachlor and pendimethalin concentrations were not significantly different over time and remained high throughout the study. Microbial respiration was suppressed in treated soils from day 21 to day 160. Soybean and canola were the most successful plant species in the germination and survival tests. Generally, with increased aging of pesticides in soil, germination time decreased. Survival time of plants increased over time for some treatments indicating possible decreased bioavailability of pesticide residues. In some cases, survival time decreased at the longer 160-day aging period, possibly indicating a change in bioavailability, perhaps as the result of formation of more bioavailable and phytotoxic metabolites. No interactive effects were noted for mixtures of pesticides compared to individually applied pesticides in terms of degradation of the parent compound or on seed germination, plant survival, or microbial respiration.

Classifying environmental pollutants: Part 3. External validation of the classification system

In order to validate a classification system for the prediction of the toxic effect concentrations of organic environmental pollutants to fish, all available fish acute toxicity data were retrieved from the ECETOC database, a database of quality-evaluated aquatic toxicity measurements created and maintained by the European Centre for the Ecotoxicology and Toxicology of Chemicals. The individual chemicals for which these data were available were classified according to the rulebase under consideration and predictions of effect concentrations or ranges of possible effect concentrations were generated. These predictions were compared to the actual toxicity data retrieved from the database. The results of this comparison show that generally, the classification system provides adequate predictions of either the aquatic toxicity (class 1) or the possible range of toxicity (other classes) of organic compounds. A slight underestimation of effect concentrations occurs for some highly water soluble, reactive chemicals with low log K(ow) values. On the other end of the scale, some compounds that are classified as belonging to a relatively toxic class appear to belong to the so-called baseline toxicity compounds. For some of these, additional classification rules are proposed. Furthermore, some groups of compounds cannot be classified, although they should be amenable to predictions. For these compounds additional research as to class membership and associated prediction rules is proposed.

Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling

Systematic toxicity testing, using conventional toxicology methodologies, of single chemicals and chemical mixtures is highly impractical because of the immense numbers of chemicals and chemical mixtures involved and the limited scientific resources. Therefore, the development of unconventional, efficient, and predictive toxicology methods is imperative. Using carcinogenicity as an end point, we present approaches for developing predictive tools for toxicologic evaluation of chemicals and chemical mixtures relevant to environmental contamination. Central to the approaches presented is the integration of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) and quantitative structure--activity relationship (QSAR) modeling with focused mechanistically based experimental toxicology. In this development, molecular and cellular biomarkers critical to the carcinogenesis process are evaluated quantitatively between different chemicals and/or chemical mixtures. Examples presented include the integration of PBPK/PD and QSAR modeling with a time-course medium-term liver foci assay, molecular biology and cell proliferation studies. Fourier transform infrared spectroscopic analyses of DNA changes, and cancer modeling to assess and attempt to predict the carcinogenicity of the series of 12 chlorobenzene isomers. Also presented is an ongoing effort to develop and apply a similar approach to chemical mixtures using in vitro cell culture (Syrian hamster embryo cell transformation assay and human keratinocytes) methodologies and in vivo studies. The promise and pitfalls of these developments are elaborated. When successfully applied, these approaches may greatly reduce animal usage, personnel, resources, and time required to evaluate the carcinogenicity of chemicals and chemical mixtures.

Pattern analysis of the variation in the sensitivity of aquatic species to toxicants

Our aim in this study was to identify groups of species showing a similar pattern in their sensitivity to toxicants and to relate the patterns to the mode of toxic action and biological species characteristics. A data matrix was composed of acute toxicity data for 26 aquatic species and 21 compounds. Most of the variation in the toxicological data was due to differences in toxicity of compounds and not intrinsic differences between species, so that practically every species can be used to order compounds with respect to average toxicity. Compounds with high overall toxicity also had large interspecies variation in sensitivity. The toxicity of non-polar narcotics correlated well with the log Kow. Compounds with a specific or reactive mode of action were more than a factor 10 toxic than predicted by their log Kow. Patterns in species sensitivity were more diffuse because only part of the variance in species sensitivity could be explained. Fishes and amphibians were more sensitive to dieldrin, lindane and pentachlorophenol than were invertebrates. Among the arthropods, the Phyllopoda (daphnids) were the most sensitive species. They were very sensitive to aniline, the heavy metals, malathion and parathion.

Analogy Models for Prediction of Human Toxicity

Estimating the toxicity to humans of chemicals by testing on human subjects is not considered to be ethically acceptable, and toxicity testing on laboratory animals is also questionable. Therefore, there is a need for alternative methods that will give estimates of various aspects of human toxicity. Batteries of in vitro tests, together with physicochemical and toxicokinetic data, analysed by efficient data analytical methods, may enable analogy models to be constructed that can predict human toxicity. It may be possible to model non-specific toxicity relating to lipophilicity, or basal cytotoxicity, for a series of diverse compounds with large variation in chemical structure and physicochemical properties. However, local models for a series of similar compounds are generally expected to be more accurate, as well as being capable of modelling more-specific interactions. Analogy models for the prediction of human toxicity are discussed and exemplified with physicochemical and cytotoxicity data from the first ten chemicals in the multicenter evaluation of in vitro cytotoxicity (MEIC) project.