Selection of non-dioxin-like PCBs for in vitro testing on the basis of environmental abundance and molecular structure

The non-dioxin-like polychlorinated biphenyls (NDL-PCBs) constitute the major proportion of PCBs found in food and human tissues. It is important to improve our understanding of the toxicity, environmental and human risks associated with the NDL-PCBs, since their toxicology is incompletely characterized and a human health risk assessment is required. This paper discusses the selection of a training set of 20 tri- to hepta-chlorinated biphenyls, PCBs 19,28,47,51,52,53,74,95,100,101,104,118,122,128,136,138,153,170,180, and 190. Suggested for comprehensive screening using in vitro assays to identify critical mechanisms of toxicological action. The selected PCBs form a balanced basis for developing of quantitative structure-activity relationship (QSAR) models for prediction of physicochemical and toxicological properties of non-tested PCB congeners. Chemical and physical properties, environmental abundance and toxicological activities of the congeners were considered during the selection process. A complementary set of PCBs, a reference set, was selected using D-optimal onion design including PCBs 18,20,28,30,37,40,50,54,60,77,82,99,122,132,153,161,170,188,192, and 193. Congeners of this set are well suited for validation of QSAR models developed using the training set. For visualization of the chemical diversity of environmentally abundant PCBs and congeners of the training and reference sets, principal component analysis (PCA) was used. Statistical molecular design was used to verify the structural representation. As a reference structure for dioxin-like PCBs, PCB 126 was added in the training set. The selected set of NDL-PCBs is proposed for use in toxicological testing programs to provide rational basis for risk assessment of the NDL-PCBs.     

Application of a computational model for Michael addition reactivity in the prediction of toxicity to Tetrahymena pyriformis

A computational model to predict acute aquatic toxicity to the ciliate Tetrahymena pyriformis has been developed. A general prediction of toxicity can be based on three consecutive steps: 1. Identification of a potential reactive mechanism via structural alerts; 2. Confirmation and quantification of (bio)chemical reactivity; 3. Establishing a relationship between calculated reactivity and toxicity. The method described herein uses a combination of a reactive toxicity (RT) model, including computed kinetic rate constants for adduct formation (log k) via a Michael acceptor mechanism of action, and baseline toxicity (BT), modelled by hydrophobicity (octanol-water partition coefficient). The maximum of the RT and BT values defines acute toxicity for a particular compound. The reactive toxicity model is based on site-specific steric and quantum chemical ground state electronic properties. The performance of the model was examined in terms of predicting the toxicity of 106 potential Michael acceptor compounds covering several classes of compounds (aldehydes, ketones, esters, heterocycles). The advantages of the computational method are described. The method allows for a closer and more transparent mechanistic insight into the molecular initiating events of toxicological endpoints.     

Regression comparisons of Tetrahymena pyriformis and Poecilia reticulata toxicity

The toxicity data of chemicals common to both the Poecilia reticulata mortality assay and the Tetrahymena pyriformis growth impairment assay were evaluated. Two chemicals were not toxic at saturation in the T. pyriformis assay. In addition, due to abiotic transformation, a third chemical was removed from further consideration. Each chemical was a priori assigned a mode of toxic action: neutral non-covalent, polar non-covalent, or electrophilic covalent toxicity. To further investigate comparisons between endpoints, polar and electrophilic chemicals were separated into class-based groups. The polar non-covalent chemicals were separated into phenols and anilines, while the electrophilic chemicals were separated into those reacting via Schiff-base formation (i.e., aldehydes) and those reacting via bimolecular substitution to a nucleophile (i.e., selected nitroaromatics). A comparison of toxic potency as a collective set was statistically described by the relationship; log(LC50(-1)) = 1.05(log(IGC50(-1))) + 0.56, n = 124; r2 = 0.85; s = 0.42; F = 682; Pr > F = 0.0001. The relationship between endpoints was inversely proportional to reactivity associated with the mode of action. While the comparative toxicity for neutral narcotics exhibited an excellent fit (r2 = 0.94), the fits for polar narcotics and electrophiles were poorer, r2 = 0.69 and 0.62, respectively. Investigations into class-based groupings indicated fit of toxic potency data for aldehydes (r2 = 0.85) and phenols (r2 = 0.81) were quite good. However, fits for anilines (r2 = 0.43) and nitroaromatics (r2 = 0.68) revealed that toxicity was not as well related between endpoints for these chemicals.     

Fuzzy clustering analysis of the first 10 MEIC chemicals

In this paper, we discuss the classification results of the toxicological responses of 32 in vivo and in vitro test systems to the first 10 MEIC chemicals. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering and a new clustering technique, namely fuzzy hierarchical cross-classification. The characteristics clustering technique produces fuzzy partitions of the characteristics (chemicals) involved and thus it is a useful tool for studying the (dis)similarities between different chemicals and for essential chemicals selection. The cross-classification algorithm produces not only a fuzzy partition of the test systems analyzed, but also a fuzzy partition of the considered 10 MEIC (multicentre evaluation of in vitro cytotoxicity) chemicals. In this way it is possible to identify which chemicals are responsible for the similarities or differences observed between different groups of test systems. In another way, there is a specific sensitivity of a chemical for one or more toxicological tests.     

Chemical and bioanalytical characterisation of PAHs in risk assessment of remediated PAH-contaminated soils

Polycyclic aromatic hydrocarbons (PAHs) are common contaminants in soil at former industrial areas; and in Sweden, some of the most contaminated sites are being remediated. Generic guideline values for soil use after so-called successful remediation actions of PAH-contaminated soil are based on the 16 EPA priority pollutants, which only constitute a small part of the complex cocktail of toxicants in many contaminated soils. The aim of the study was to elucidate if the actual toxicological risks of soil samples from successful remediation projects could be reflected by chemical determination of these PAHs. We compared chemical analysis (GC-MS) and bioassay analysis (H4IIE-luc) of a number of remediated PAH-contaminated soils. The H4IIE-luc bioassay is an aryl hydrocarbon (Ah) receptor-based assay that detects compounds that activate the Ah receptor, one important mechanism for PAH toxicity. Comparison of the results showed that the bioassay-determined toxicity in the remediated soil samples could only be explained to a minor extent by the concentrations of the 16 priority PAHs. The current risk assessment method for PAH-contaminated soil in use in Sweden along with other countries, based on chemical analysis of selected PAHs, is missing toxicologically relevant PAHs and other similar substances. It is therefore reasonable to include bioassays in risk assessment and in the classification of remediated PAH-contaminated soils. This could minimise environmental and human health risks and enable greater safety in subsequent reuse of remediated soils.     

Influence of hydroxypropylcyclodextrins on the toxicity of mixtures

We studied the influence of hydroxypropylcyclodextrins (HPCDs) on the toxicity of some mixtures. Using the Photobacterium phosphoreum toxicity test, the joint toxicological effect for Mixture I (containing p-nitrobenzaldehyde and 1-nitronaphthalene) and Mixture II (containing p-nitrobenzaldehyde and malononitrile) were determined in water and in aqueous solutions of HPCDs. The results indicate that, although the toxicological joint effect for Mixture I (simple addition) differs from that of Mixture II (synergism), alpha- and beta-HPCD can significantly reduce the toxicity of the test compounds, whereas gamma-HPCD has only a slight effect. Explanations for these observations are given that invoke the molecular structure of the individual chemicals as well as the structures of HPCDs. This provides information to assist the application of HPCDs in remediation of environmental pollution.     

Research to develop a predicting system of mammalian subacute toxicity (3) construction of a predictive toxicokinetics model

A new predictive toxicokinetics model was developed to estimate subacute toxicity (target organs, severity, etc.) of non-congeneric industrial chemicals, where the chemical structures and physico-chemical properties are only available. Thus, a physiological pharmacokinetics model, which consists of blood, liver, kidney (these were experimentally found as major toxicological targets), muscle and fat compartments , was established to simulate the chemical concentrations in organs/tissues with pharmacokinetic parameters by means of Runge-Kutta-Gill algorithm. The pliarmacokinetic parameters, i.e. absorption rate, absorption ratio, hepatic extraction ratio of metabolism and renal clearance were calculated by using separately established Quantitative Structure-Pharmacokinetics Relationship equations. The developed predictive model was then applied to simulations of 43 non-congeneric industrial chemicals. The chemical concentrations in organs/tissues after single oral administration were simulated, and their maximum concentrations (Cmax's) and area tinder the concentration-time curves (AUC's) were calculated.Fast Inverse Laplace Transform was newly applied for the purpose of simulation of 28-day repeated dose toxicity.Simulated concentrations of 28 days repeated dose were, however, found to be the same as those of simple repetitions of a single administration per day because of the short half-lives of non-congeneric industrial chemicals.A comparison of subacute toxicity data with Cmax's and AUC's in a single dose scenario suggested that the organs/tissues with relatively high concentrations of tested chemical substances were the most sensitive targets within a chemical.Chemical concentrations in liver, for instance, were correlated with the severity of hepatotoxicity among the chemicals. It was also suggested that to improve and widen the present approach, data of metabolite and reactivity of non-congeneric industrial chemicals to organs/tissues, receptors, etc. should be incorporated into the model.     

Multi-generation studies with Chironomus riparius--effects of low tributyltin concentrations on life history parameters and genetic diversity

While toxicological data are available for numerous chemicals from standard tests, little is known on effects of pollutants over several generations or regarding chronic effects of chemicals on genetic diversity. Within the experiments, effects of the model pollutant tributyltin (TBT) were investigated over eleven generations at a sublethal TBT concentration of 4.46 μg as Sn kg⁻¹ sediment dw on life-cycle parameters and genetic variability of Chironomus riparius. Moreover, the adaptation potential towards TBT was determined. This experimental design enables the identification of TBT effects on life-cycle parameters and the determination of a potential extinction risk caused by chronic exposure. Furthermore, effects on the genetic structure can be determined, which are not predictable based solely on knowledge of the toxic mode of action of the chemical.

Genetic variety was determined via microsatellite analysis, measuring individual length differences of highly variable satellite DNA fragments. For the identification of changes in tolerances towards the stressor, acute and chronic toxicity experiments were conducted.

During the multi-generation study, significant effects on development and reproduction were determined. For some generations, the emergence was significantly (p < 0.05) delayed under TBT exposure. Reproduction seems to be a sensitive parameter as well, whereby females laid significantly larger egg masses (p < 0.05) in the latter generations. TBT did not affect the population growth rate nor the genetic variability, while clear deviations from the Hardy–Weinberg equilibrium appeared. The study also provides strong evidence for the acquirement of a higher tolerance towards the stressor in the TBT-exposed group.     

Discrimination of excess toxicity from narcotic effect: Comparison of toxicity of class-based organic chemicals to Daphnia magna and Tetrahymena pyriformis

The discrimination of excess toxicity from narcotic effect plays a crucial role in the study of modes of toxic action for organic compounds. In this paper, the toxicity data of 758 chemicals to Daphnia magna and 993 chemicals to Tetrahymena pyriformis were used to investigate the excess toxicity. The result showed that mode of toxic action of chemicals is species dependent. The toxic ratio (TR) calculated from baseline model over the experimentally determined values showed that some classes (e.g. alkanes, alcohols, ethers, aldehydes, esters and benzenes) shared same modes of toxic action to both D. magna and T. pyriformis. However, some classes may share different modes of toxic action to T. pyriformis and D. magna (e.g. anilines and their derivatives). For the interspecies comparison, same reference threshold need to be used between species toxicity. The excess toxicity indicates that toxicity enhancement is driven by reactive or specific toxicity. However, not all the reactive compounds exhibit excess toxicity. In theory, the TR threshold should not be related with the experimental uncertainty. The experimental uncertainty only brings the difficulty for discriminating the toxic category of chemicals. The real threshold of excess toxicity which is used to identify baseline from reactive chemicals should be based on the critical concentration difference inside body, rather than critical concentration outside body (i.e. EC₅₀ or IGC₅₀). The experimental bioconcentration factors can be greatly different from predicted bioconcentration factors, resulting in different toxic ratios and leading to mis-classification of toxic category and outliers.