Ecotoxicity interspecies QAAR models from Daphnia toxicity of pharmaceuticals and personal care products.

Pharmaceutical and Personal Care Products (PPCPs) became a class of contaminants of emerging concern because are ubiquitously detected in surface water and soil, where they can affect wildlife. Ecotoxicological data are only available for a few PPCPs, thus modelling approaches are essential tools to maximize the information contained in the existing data. In silico methods may be helpful in filling data gaps for the toxicity of PPCPs towards various ecological indicator organisms. The good correlation between toxicity toward Daphnia magna and those on two fish species (Pimephales promelas and Oncorhynchus mykiss), improved by the addition of one theoretical molecular descriptor, allowed us to develop predictive models to investigate the relationship between toxicities in different species. The aim of this work is to propose quantitative activity-activity relationship (QAAR) models, developed in QSARINS and validated for their external predictivity. Such models can be used to predict the toxicity of PPCPs to a particular species using available experimental toxicity data from a different species, thus reducing the tests on organisms of higher trophic level. Similarly, good QAAR models, implemented by molecular descriptors to improve the quality, are proposed here for fish interspecies. We also comment on the relevance of autocorrelation descriptors in improving all studied interspecies correlations.

QSAR prediction of the competitive interaction of emerging halogenated pollutants with human transthyretin.

The determination of the potential endocrine disruption (ED) activity of chemicals such as poly/perfluorinated compounds (PFCs) and brominated flame retardants (BFRs) is still hindered by a limited availability of experimental data. Quantitative structure-activity relationship (QSAR) strategies can be applied to fill this data gap, help in the characterization of the ED potential, and screen PFCs and BFRs with a hazardous toxicological profile. This paper proposes the modelling of T4-TTR (thyroxin-transthyretin) competing potency and relative binding potency toward T4 (logT4-REP) of PFCs and BFRs by regression and classification QSAR models. This study is a follow up of a former work, which analysed separately the interaction of BFRs and PFCs with the carrier TTR. The new results demonstrate the possibility of developing robust and predictive QSARs, which include both BFRs and PFCs in the training set, obtaining larger applicability domains than the existing models developed separately for BFRs and PFCs. The selection of modelling molecular descriptors confirms the importance of structural features, such as the aromatic OH or the molecular length, to increase the binding of the studied chemicals to TTR. Additionally, the need of experimental tests for some chemicals, and in particular for some of the BFRs, is highlighted.

Epistaxis in children under the age of two: possible marker of abuse/neglect? A retrospective study in North-Eastern Piedmont hospitals.

AIM: Epistaxis is an extremely common event at all ages; however, under two years of age epistaxis is a very rare event and recent studies carried out in Great Britain concern this event as related to possible non-accidental trauma. To date, no other studies carried out in Italy are available on this topic. METHODS: A file review of all cases of epistaxis occurred in children under the age of 2 who were admitted into the ED in our area over a period of two years was carried out. RESULTS: We have collected data concerning 10 cases of epistaxis occurred in children under 2 years of age with an incidence of 10.4 cases per 10000 accessions to the ED of children under the age of 2. Four of the cases had attendances for head injury or facial trauma. CONCLUSION: The results obtained are higher than the results of the British studies adopting the same methodology, but comparable to their surveillance data on the general population. Through the analysis of the collected data, two correlated assumptions have been made: a possible relationship between epistaxis and neglect, and a relation between epistaxis and domestic accidents.

QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds.

Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure-activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption.

Modelling physico-chemical properties of (benzo)triazoles, and screening for environmental partitioning.

(Benzo)triazoles are distributed throughout the environment, mainly in water compartments, because of their wide use in industry where they are employed in pharmaceutical, agricultural and deicing products. They are hazardous chemicals that adversely affect humans and other non-target species, and are on the list of substances of very high concern (SVHC) in the new European regulation of chemicals - REACH (Registration, Evaluation, Authorization and Restriction of Chemical substances). Thus there is a vital need for further investigations to understand the behavior of these compounds in biota and the environment. In such a scenario, physico-chemical properties like aqueous solubility, hydrophobicity, vapor pressure and melting point can be useful. However, the limited availability and the high cost of lab testing prevents the acquisition of necessary experimental data that industry must submit for the registration of these chemicals. In such cases a preliminary analysis can be made using Quantitative Structure-Property Relationships (QSPR) models. For such an analysis, we propose Multiple Linear Regression (MLR) models based on theoretical molecular descriptors selected by Genetic Algorithm (GA). Training and prediction sets were prepared a priori by splitting the available experimental data, which were then used to derive statistically robust and predictive (both internally and externally) models. These models, after verification of their structural applicability domain (AD), were used to predict the properties of a total of 351 compounds, including those in the REACH preregistration list. Finally, Principal Component Analysis was applied to the predictions to rank the environmental partitioning properties (relevant for leaching and volatility) of new and untested (benzo)triazoles within the AD of each model. Our study using this approach highlighted compounds dangerous for the aquatic compartment. Similar analyses using predictions obtained by the EPI Suite and VCCLAB tools are also compared and discussed in this paper.

QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs.

Endocrine disrupting chemicals (EDCs) are suspected of posing serious threats to human and wildlife health through a variety of mechanisms, these being mainly receptor-mediated modes of action. It is reported that some EDCs exhibit dual activities as estrogen receptor (ER) and androgen receptor (AR) binders. Indeed, such compounds can affect the normal endocrine system through a dual complex mechanism, so steps should be taken not only to identify them a priori from their chemical structure, but also to prioritize them for experimental tests in order to reduce and even forbid their usage. To date, very few EDCs with dual activities have been identified. The present research uses QSARs, to investigate what, so far, is the largest and most heterogeneous ER binder data set (combined METI and EDKB databases). New predictive classification models were derived using different modelling methods and a consensus approach, and these were used to virtually screen a large AR binder data set after strict validation. As a result, 46 AR antagonists were predicted from their chemical structure to also have potential ER binding activities, i.e. pleiotropic EDCs. In addition, 48 not yet recognized ER binders were in silico identified, which increases the number of potential EDCs that are substances of very high concern (SVHC) in REACH. Thus, the proposed screening models, based only on structure information, have the main aim to prioritize experimental tests for the highlighted compounds with potential estrogenic activities and also to design safer alternatives.

Quantitative structure-activity relationship modelling of oral acute toxicity and cytotoxic activity of fragrance materials in rodents.

Fragrance materials are used as ingredients in many consumer and personal care products. The wide and daily use of these substances, as well as their mainly uncontrolled discharge through domestic sewage, make fragrance materials both potential indoor and outdoor air pollutants which are also connected to possible toxic effects on humans (asthma, allergies, headaches). Unfortunately, little is known about the environmental fate and toxicity of these substances. However, the use of alternative, predictive approaches, such as quantitative structure-activity relationships (QSARs), can help in filling the data gap and in the characterization of the environmental and toxicological profile of these substances. In the proposed study, ordinary least squares regression-based QSAR models were developed for three toxicological endpoints: mouse oral LD(50), inhibition of NADH-oxidase (EC(50) NADH-Ox) and the effect on mitochondrial membrane potential (EC(50) DeltaPsim). Theoretical molecular descriptors were calculated by using DRAGON software, and the best QSAR models were developed according to the principles defined by the Organization for Economic Co-operation and Development.

Externally validated QSPR modelling of VOC tropospheric oxidation by NO3 radicals.

The troposphere is the principal recipient of volatile organic chemicals (VOCs) of both anthropogenic and biogenic origin. The persistence of these compounds in the troposphere is an important factor for the evaluation of their fate, and the possible negative effects to the environment and human health. In this study, the tropospheric lifetime of 166 VOCs, in terms of night-time degradation rates with nitrate radical (NO(3)), was modelled by the quantitative structure-property relationships (QSPR) approach. The multiple linear regression method was applied, in combination with the genetic algorithm-variable subset selection procedure, to a variety of theoretical molecular descriptors, calculated by the DRAGON software. The models were developed according to the OECD principles for regulatory acceptance of QSARs, with particular attention to external validation and applicability domain (AD). The external validation was performed on an unbiased external test set or by splitting the available data using self-organized maps or the random by response approach. The best QSPR models presented in this study showed good internal (range of Q(loo)(2): 89-92%) as well as external predictivity (range of Q(ext)(2): 75-89%). The AD of the models was analysed by the leverage approach, and was represented graphically in the Williams graph.

Prediction of PAH mutagenicity in human cells by QSAR classification.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants of high environmental concern. The experimental data of a mutagenicity test on human B-lymphoblastoid cells (alternative to the Ames bacterial test) for a set of 70 oxo-, nitro- and unsubstituted PAHs, detected in particulate matter (PM), were modelled by Quantitative Structure-Activity Relationships (QSAR) classification methods (k-NN, k-Nearest Neighbour, and CART, Classification and Regression Tree) based on different theoretical molecular descriptors selected by Genetic Algorithms. The best models were validated for predictivity both externally and internally. For external validation, Self Organizing Maps (SOM) were applied to split the original data set. The best models, developed on the training set alone, show good predictive performance also on the prediction set chemicals (sensitivity 69.2-87.1%, specificity 62.5-87.5%). The classification of PAHs according to their mutagenicity, based only on a few theoretical molecular descriptors, allows a preliminary assessment of the human health risk, and the prioritisation of these compounds.

QSAR modelling of water quality indices of alkylphenol pollutants.

The aim of this study was to determine the degradability of 26 Alkylphenols (APs) by Chemical Oxygen Demand (COD) and/or 5-day Biochemical Oxygen Demand (BOD(5)), and to describe these data from Quantitative Structure-activity Relationships (QSARs). Statistical analysis techniques, such as Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least-Squares (PLS) Regression and Neural Network (NN) were carried out to calibrate and validate four-descriptor QSAR models using two different types of descriptor sets. Stable MLR-QSAR models using Leave-One-Out (LOO) were obtained with high predictability performance: r(2) = 0.924, Q(2)(cv) =0.854 for log (1/BOD) model on 24 APs and r(2) = 0.888, Q(2)(cv) = 0.818 for log (1/COD) on all the studied APs. The MLR models, built with four Dragon descriptors selected by Genetic Algorithm (GA), presented the following performances on 24 APs: r(2) = 0.889, Q(2)(cv) = 0.848 for log (1/BOD(5)) and r(2) = 0.885, Q(2)(cv) = 0.834 for log (1/COD) on 26 compounds. From these results, it is expected that the QSAR models generated could be successfully expanded to predict the biological and chemical activities of structurally diverse AP compounds.

Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity.

Nitrated Polycyclic Aromatic Hydrocarbons (nitro-PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A set of mutagenicity data (TA100) for 48 nitro-PAHs was modeled by the Quantitative Structure-Activity Relationships (QSAR) regression method, and OECD principles for QSAR model validation were applied. The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors. The models were validated for predictivity by both internal and external validation. For the external validation, three different splitting approaches, D-optimal Experimental Design, Self Organizing Maps (SOM) and Random Selection by activity sampling, were applied to the original data set in order to compare these methodologies and to select the best descriptors able to model each prediction set chemicals independently of the splitting method applied. The applicability domain was verified by the leverage approach.

Linear QSAR regression models for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors.

The development of QSAR models useful for the prediction of fish bioconcentration factor (BCF) for a wide range of different chemical classes is crucial for the assessment and prioritisation of potentially persistent bioaccumulative and toxic substances. In this study we present QSAR models for BCF developed on a wide range of chemical structural classes of environmental and toxicological interest (such as dyes and various chlorinated and brominated compounds). The aim is to provide valid QSAR models, statistically validated for predictivity, for the prediction of BCF in general, but also for problematical chemical classes such as highly hydrophobic chemicals. Several descriptors, calculated by different commercially available software packages, have been employed in order to take into account relevant information provided by physicochemical properties (octanol/water partition coefficient and water solubility) and molecular features (structural and quantum-chemical molecular descriptors). The best descriptor subsets for the models were selected using the Genetic Algorithm-Variable Subset Selection strategy (GA-VSS) and calculations were performed by ordinary least squares regression. Starting from a data set of 640 compounds (logK(ow) range from -2.34 to 12.66), we developed linear QSARs, firstly for a data set of 620 compounds (logK(ow) range from -2.34 to 10.35) and secondly specifically for 87 highly hydrophobic chemicals (logK(ow) range from 6.00 to 10.35). All these models have been statistically validated (both internally by cross-validation and bootstrap and externally, by "a priori" splitting of available data by Kohonen Map-ANN in training and prediction sets) and their structural chemical domain has been verified by the leverage approach.

Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study.

The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.

Prediction of aromatic amines mutagenicity from theoretical molecular descriptors.

In the present research the mutagenicity data (Ames tests TA98 and TA100) for various aromatic and heteroaromatic amines, a data set extensively studied by other quantitative structure-activity relationship (QSAR)-authors, have been modeled by a wide set of theoretical molecular descriptors using linear multivariate regression (MLR) and genetic algorithm-variable subset selection (GA-VSS). The models have been calculated on a subset of compounds selected by a D-optimal experimental design. Moreover, they have been validated by both internal and external validation procedures showing satisfactory predictive performance. The models proposed here can be useful in predicting data and setting a testing priority for those compounds for which experimental data are not available or are not yet synthesized.

Joint algal toxicity of 16 dissimilarly acting chemicals is predictable by the concept of independent action.

For a predictive assessment of the aquatic toxicity of chemical mixtures, two competing concepts are available: concentration addition and independent action. Concentration addition is generally regarded as a reasonable expectation for the joint toxicity of similarly acting substances. In the opposite case of dissimilarly acting toxicants the choice of the most appropriate concept is a controversial issue. In tests with freshwater algae we therefore studied the extreme situation of multiple exposure to chemicals with strictly different specific mechanisms of action. Concentration response analyses were performed for 16 different biocides, and for mixtures containing all 16 substances in two different concentration ratios. Observed mixture toxicity was compared with predictions, calculated from the concentration response functions of individual toxicants by alternatively applying both concepts. The assumption of independent action yielded accurate predictions, irrespective of the mixture ratio or the effect level under consideration. Moreover, results even demonstrate that dissimilarly acting chemicals can show significant joint effects, predictable by independent action, when combined in concentrations below individual NOEC values, statistically estimated to elicit insignificant individual effects of only 1%. The alternative hypothesis of concentration addition resulted in overestimation of mixture toxicity, but differences between observed and predicted effect concentrations did not exceed a factor of 3.2. This finding complies with previous studies, which indicated near concentration-additive action of mixtures of dissimilarly acting substances. Nevertheless, with the scientific objective to predict multi-component mixture toxicity with the highest possible accuracy, concentration addition obviously is no universal solution. Independent action proves to be superior where components are well known to interact specifically with different molecular target sites, and provided that reliable statistical estimates of low toxic effects of individual mixture constituents can be given. With a regulatory perspective, however, fulfilment of both conditions may be regarded as an extraordinary situation, and hence concentration addition may be defendable as a pragmatic and precautionary default assumption.

Water quality objectives for mixtures of toxic chemicals: problems and perspectives.

The need to develop water quality objectives not only for single substances but also for mixtures of chemicals seems evident. For that purpose, the conceptual basis could be the use of the two existing biometric models: concentration addition (CA) and independent action (IA), which is also called response addition. Both may allow calculation of the toxicity of mixtures of chemicals with similar modes of action (CA) or dissimilar modes of action (IA), respectively. The joint research project Prediction and Assessment of the Aquatic Toxicity of Mixtures of Chemicals (PREDICT) within the framework of the IVth Environment and Climate Programme of the European Commission, provided the opportunity to address (a) chemometric and QSAR criteria to classify substances as supposedly similarly or dissimilarly acting; (b) the predictive values of both models for the toxicity of mixtures at low, statistically nonsignificant effect concentrations of the individual components; and (c) the predictability of mixture toxicity at higher levels of biological complexity. In this article, the general outline, methodological approach, and some preliminary findings of PREDICT are presented. A procedure for classifying chemicals in relation to their structural and toxicological similarities has been developed. The predictive capabilities of CA and IA models have been demonstrated for single species and, to some extent, for multispecies testing. The role of very low effect concentrations in multiple mixtures has been evaluated. Problems and perspectives concerning the development of water quality objectives for mixtures are discussed.

Classification of environmental pollutants for global mobility potential.

The environmental behaviour of global organic contaminants is known to be controlled by the physico-chemical properties of the compounds themselves. The principal component analysis of some physico-chemical properties, particularly relevant in determining mobility potential (vapour pressure, Henry's law constant, water solubility, K(OW), K(OA) and melting point) allows a multivariate approach to a ranking of organic pollutants according to their intrinsic tendency towards mobility, and the definition of four a priori mobility classes for screening purposes. Quantitative structure-property relationships (QSPRs) were used to predict missing values for octanol/air partition coefficients. Finally, a classification method employing theoretical molecular descriptors was used to assign studied chemicals to four mobility classes. The proposed approach assesses, directly and simply, a pollutant's inherent tendency towards mobility using only knowledge of the pollutant's molecular structure; the approach is particularly useful for a preliminary screening and the prioritisation of organic pollutants of emerging environmental concern.

Ranking of volatile organic compounds for tropospheric degradability by oxidants: a QSPR approach.

The limited availability and variability of data related to atmospheric degradation reaction is a very relevant issue in studies related to environmental fate and behavior of chemicals. For screening purposes, the experimental data of the oxidation rate constants for the reactions with the radicals OH, NO3 and with ozone of 65 heterogeneous organic compounds were explored by Principal Component Analysis: a ranking of volatile organic chemicals (VOC) according to their relative overall atmospheric degradability and an atmospheric persistence index (ATPIN) is proposed. This index has been modeled by theoretical molecular descriptors to obtain MLR models with high predictive power, both internally and externally validated, and the definition of chemical domain applicability. This procedure allows a fast ranking of VOCs according to their tendency to be degraded by atmospheric oxidants, starting only from the knowledge of their molecular structure.

Predicting the joint algal toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants.

Herbicidal s-triazines are widespread contaminants of surface waters. They are highly toxic to algae and other primary producers in aquatic systems. This results from their specific interference with photosynthetic electron transport. Risk assessment for aquatic biota has to consider situations of simultaneous exposure to various of these toxicants. In tests with freshwater algae we predicted and determined the toxicity of multiple mixtures of 18 different s-triazines. The toxicity parameter was the inhibition of reproduction of Scenedesmus vacuolatus. Concentration-response analyses were performed for single toxicants and for mixtures containing all 18 s-triazines in two different concentration ratios. Experiments were designed to allow a valid statistical description of the entire concentration-response relationships, including the low concentration range down to EC1. Observed effects and effect concentrations of mixtures were compared to predictions of mixture toxicity. Predictions were calculated from the concentration-response functions of individual s-triazines by applying the concepts of concentration addition and independent action (response addition) alternatively. Predictions based on independent action tend to underestimate the overall toxicity of s-triazine mixtures. In contrast, the concept of concentration addition provides highly accurate predictions of s-triazine mixture toxicity, irrespective of the effect level under consideration and the concentration ratio of the mixture components. This also holds true when the mixture components are present in concentrations below their individual NOEC values. Concentrations statistically estimated to elicit non-significant effects of only 1% still contribute to the overall toxicity. When present in a multi-component mixture they can co-operate to give a severe joint effect. Applicability of the findings obtained with s-triazines to mixtures of other contaminants in aquatic systems and consequences for risk assessment procedures are discussed.

QSAR and chemometric approaches for setting water quality objectives for dangerous chemicals.

In order to evaluate environmentally safe levels of dangerous chemicals, there is the need for a set of toxicological data on organisms representative of the ecosystems, which is often unavailable or inadequate. In this article, a predictive approach was applied to a set of 125 chemicals (derived from the European priority list in compliance with Directive 76/464/EEC), for which water quality objectives were available. Toxicological data on organisms representative of the aquatic environment (algae, Daphnia, and fish) were taken from the literature or predicted by means of quantitative structure--activity relationships. This provided toxicological data on all three organisms for 97 of 125 chemicals and on at least two organisms (Daphnia and fish) for the whole data set. Principal Component Analysis was applied in order to perform an a priori classification of chemicals based on toxicity data. Then several classification models, based on traditional and nontraditional molecular descriptors, were applied. Classification models gave results in agreement with the a priori classification as well as with the original water quality objectives classification. The behavior of some outliers was explained. The approach described appears to be a useful tool for the preliminary classification of chemicals that are dangerous to the aquatic environment for which toxicological data are inadequate.