The QSPR-THESAURUS: the online platform of the CADASTER project.

The aim of the CADASTER project (CAse Studies on the Development and Application of in Silico Techniques for Environmental Hazard and Risk Assessment) was to exemplify REACH-related hazard assessments for four classes of chemical compound, namely, polybrominated diphenylethers, per and polyfluorinated compounds, (benzo)triazoles, and musks and fragrances. The QSPR-THESAURUS website (http: / /qspr-thesaurus.eu) was established as the project's online platform to upload, store, apply, and also create, models within the project. We overview the main features of the website, such as model upload, experimental design and hazard assessment to support risk assessment, and integration with other web tools, all of which are essential parts of the QSPR-THESAURUS.

Description and propagation of uncertainty in input parameters in environmental fate models.

Today, chemical risk and safety assessments rely heavily on the estimation of environmental fate by models. The key compound-related properties in such models describe partitioning and reactivity. Uncertainty in determining these properties can be separated into random and systematic (incompleteness) components, requiring different types of representation. Here, we evaluate two approaches that are suitable to treat also systematic errors, fuzzy arithmetic, and probability bounds analysis. When a best estimate (mode) and a range can be computed for an input parameter, then it is possible to characterize the uncertainty with a triangular fuzzy number (possibility distribution) or a corresponding probability box bound by two uniform distributions. We use a five-compartment Level I fugacity model and reported empirical data from the literature for three well-known environmental pollutants (benzene, pyrene, and DDT) as illustrative cases for this evaluation. Propagation of uncertainty by discrete probability calculus or interval arithmetic can be done at a low computational cost and gives maximum flexibility in applying different approaches. Our evaluation suggests that the difference between fuzzy arithmetic and probability bounds analysis is small, at least for this specific case. The fuzzy arithmetic approach can, however, be regarded as less conservative than probability bounds analysis if the assumption of independence is removed. Both approaches are sensitive to repeated parameters that may inflate the uncertainty estimate. Uncertainty described by probability boxes was therefore also propagated through the model by Monte Carlo simulation to show how this problem can be avoided.

PLS-optimal: a stepwise D-optimal design based on latent variables.

Several applications, such as risk assessment within REACH or drug discovery, require reliable methods for the design of experiments and efficient testing strategies. Keeping the number of experiments as low as possible is important from both a financial and an ethical point of view, as exhaustive testing of compounds requires significant financial resources and animal lives. With a large initial set of compounds, experimental design techniques can be used to select a representative subset for testing. Once measured, these compounds can be used to develop quantitative structure-activity relationship models to predict properties of the remaining compounds. This reduces the required resources and time. D-Optimal design is frequently used to select an optimal set of compounds by analyzing data variance. We developed a new sequential approach to apply a D-Optimal design to latent variables derived from a partial least squares (PLS) model instead of principal components. The stepwise procedure selects a new set of molecules to be measured after each previous measurement cycle. We show that application of the D-Optimal selection generates models with a significantly improved performance on four different data sets with end points relevant for REACH. Compared to those derived from principal components, PLS models derived from the selection on latent variables had a lower root-mean-square error and a higher Q2 and R2. This improvement is statistically significant, especially for the small number of compounds selected.

Variability and uncertainty in Swedish exposure factors for use in quantitative exposure assessments.

Information of exposure factors used in quantitative risk assessments has previously been compiled and reported for U.S. and European populations. However, due to the advancement of science and knowledge, these reports are in continuous need of updating with new data. Equally important is the change over time of many exposure factors related to both physiological characteristics and human behavior. Body weight, skin surface, time use, and dietary habits are some of the most obvious examples covered here. A wealth of data is available from literature not primarily gathered for the purpose of risk assessment. Here we review a number of key exposure factors and compare these factors between northern Europe--here represented by Sweden--and the United States. Many previous compilations of exposure factor data focus on interindividual variability and variability between sexes and age groups, while uncertainty is mainly dealt with in a qualitative way. In this article variability is assessed along with uncertainty. As estimates of central tendency and interindividual variability, mean, standard deviation, skewness, kurtosis, and multiple percentiles were calculated, while uncertainty was characterized using 95% confidence intervals for these parameters. The presented statistics are appropriate for use in deterministic analyses using point estimates for each input parameter as well as in probabilistic assessments.

Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .

Conjoint analysis for environmental evaluation--a review of methods and applications.

BACKGROUND, AIM AND SCOPE: Conjoint analysis and the related choice-modelling methods have been used for many years in marketing research to evaluate consumer behaviour and preferences for different kinds of product attributes. Recently, the number of applications in environmental science and management has started to grow. Conjoint analysis is found in many different forms, and the environmental studies evaluated in this review display the same range of methods as in other fields. The key characteristic of all these methods is that trade-offs are evaluated by jointly considering a number of important attributes. MAIN FEATURES: This paper is a review of the literature on environmental applications of conjoint analysis and assesses in which environmental area conjoint analysis has been most successful. The method and the design of the studies are reviewed as well. RESULTS: A total of 84 studies were found, dealing with environmental issues that were evaluated by conjoint analysis. The studies concern agriculture, ecosystem management, energy, environmental evaluation, forestry, land management, pollution, products, recreation, environmental risk analysis and waste management. DISCUSSION: Choice experiments seem to have a comparatively stronger position in environmental studies than elsewhere. Most of the environmental applications are related to natural resource management. This is somewhat surprising, but a number of reports have appeared also on product evaluation, which could be a key application area in the future. CONCLUSIONS: Compared to marketing and transportation, the number of environmental conjoint studies is rather small but increasing, and the method has proven to work effectively in eliciting preferences on environmental issues. In environmental issues, experimenters often use choice experiments, especially concerning ecosystem management and environmental evaluations. When it comes to evaluating preferences concerning agriculture, forestry, energy and products, a more traditional approach of conjoint analysis is favoured. RECOMMENDATIONS AND PERSPECTIVES: Two new areas of application are identified in this review--environmental communication and expert elicitation. Conjoint analysis can thus be developed into a useful instrument for environmental risk analysis and communication, both of which are necessary for an efficient approach to risk governance.

Low-temperature formation and degradation of chlorinated benzenes, PCDD and PCDF in dust from steel production.

Dust from thermal processes may catalytically enhance the formation of chlorinated aromatic compounds under oxygen-rich conditions. The activities of two dust samples from electric arc furnaces and one from iron ore-based steelmaking (oxygen converter) were investigated in a laboratory experiment. The dust samples were heated at 300 degrees C for 2 h in an air atmosphere. The concentrations of chlorinated benzenes did not change greatly upon heating, while the concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans decreased. The addition of copper in parallel runs resulted in a substantial increase in the concentration of chlorinated benzenes, thus indicating that the experimental setup was suitable for the evaluation of low-temperature catalysis. The outcome of the experiment seems to suggest that results cannot easily be extrapolated between different thermal and metallurgical processes. Some measures to reduce emissions, such as inhibition of catalytic activity and rapid cooling, could possibly be counterproductive when applied to off-gases from the steelmaking processes investigated here.

Virtual screening for environmental pollutants: structure-activity relationships applied to a database of industrial chemicals.

The current risk paradigm calls for individual consideration and evaluation of each separate environmental pollutant, but this does not reflect accurately the cumulative impact of anthropogenic chemicals. In the present study, previously validated structure-activity relationships were used to estimate simultaneously the baseline toxicity and atmospheric persistence of approximately 50,000 compounds. The results from this virtual screening indicate fairly stable statistical distributions among small anthropogenic compounds. The baseline toxicity was not changed much by halogen substitution, but a distinct increase seemed to occur in the environmental persistence with increased halogenation. The ratio of the atmospheric half-lives to the median lethal concentrations provides a continuous scale with which to rank and summarize the incremental environmental impacts in a mixture-exposure situation. Halogenated compounds as a group obtained a high ranking in this data set, with well-known pollutants at the very top: DDT metabolites and derivatives, polychlorinated biphenyls, diphenyl ethers and dibenzofurans, chlorinated paraffins, chlorinated benzenes and derivatives, hydrochlorofluorocarbons, and dichlorononylphenol. Environmentally friendly chemicals that obtained the lowest rank are nearly all hydroxylated and water-soluble. Virtual screening can assist with "green chemistry" in designing safe and degradable products and enable assessment of the efficiency in chemicals risk management.

Halogenated aromatics from steel production: results of a pilot-scale investigation.

The potential environmental impact of emissions of halogenated aromatics from the steel industry is of growing concern. It has been suggested that electric arc furnaces are the only industrial source with constant or increasing emissions of dioxins to air. Here the results are reported from a pilot plant study on how scrap composition and various treatment alternatives affect the formation and release of chlorinated and brominated aromatics. The experiments were conducted with a statistical mixture design, and it is shown that scrap composition has a significant impact on the outcome. In contrast, the various treatment schemes examined--shredding, disassembly, and briquetting--did not affect the formation and release of halogenated aromatics. Parallel experiments with injection of adsorbents showed that it is possible to reduce emissions without substantial investments, and this option is recommended as a low-cost solution.

Distribution and levels of brominated flame retardants in sewage sludge.

One hundred and sixteen sewage sludge samples from 22 municipal wastewater treatment plants in Sweden were analysed for brominated flame retardants. Polybrominated diphenyl ethers (PBDEs) were in the range n.d.-450 ng/g wet weight, tetrabromobisphenol A (TBBPA) varied between n.d. and 220 ng/g wet weight, 2,4,6-tribromophenol was in the range n.d.-0.9 ng/g wet weight and polybrominated biphenyls were not detected (except for a possible analytical interference). There was a significant variation in the samples among plants. Influence from industries and other local sources can therefore be assumed. The correlation pattern indicated contribution from three different types of technical products; composed of either low-brominated PBDEs, decaBDE or TBBPA.

Prediction of vapour pressures for halogenated diphenyl ether congeners from molecular descriptors.

BACKGROUND, AIM AND SCOPE: Polychlorinated diphenyl ethers (PCDE) and polybrominated diphenyl ethers (PBDE) have both been identified as environmental contaminants. The physical properties are important in determining the distribution and fate of organic contaminants in the environment. The purpose of the present investigation was to characterise halogenated diphenyl ethers using computationally derived descriptors, and to develop calibration models for the vapour pressure from published experimental data. METHODS: Experimental data for vapour pressures were obtained from the literature. The chemical structure of each PCDE and PBDE congener was optimised prior to descriptor generation. The data analysis was performed using principal component analysis (PCA) and partial least squares regression (PLSR). The calibration models were validated with external test sets. RESULTS AND DISCUSSION: All congeners of PCDEs and PBDEs were characterised by 795 molecular descriptors and two principal components could account for about two thirds of the variance within each group. Bilinear calibration models were developed that could explain 99.4% of the variance in the external validation test sets. Vapour pressures were subsequently predicted for all congeners that were adequately described by these calibration models. The type and number of halogen atoms in the molecule were the main factors influencing the vapour pressures of halogen substituted diphenyl ethers, but the variations in substitution pattern was also shown to be a significant factor. CONCLUSIONS: The molecular descriptor patterns of halogenated aromatic compounds such as diphenyl ethers can be described and interpreted using principal component analysis (PCA). The major sources of variation in the descriptor spaces for PCDEs and PBDEs are the same as those contributing to the differences in vapour pressure, similar to what has previously been reported for the PCBs. The bilinear calibration models for vapour pressure presented here, has a standard error of prediction that is lower than what is reported as the experimental uncertainty or observed as deviations between experimental investigations. The estimated prediction errors are expected to be within the reported boundaries when the models are applied to new objects within the same molecular descriptor space, and model predictions can hence extend the current database of experimental values. RECOMMENDATIONS AND OUTLOOK: The results from this investigation and others show that the establishment of quantitative structure-property relationships (QSPR) is a viable approach to estimate physical properties for halogenated diphenyl ethers. It is easy to foresee an increased need for using QSPR estimation methods in the future, for evaluation of the environmental fate for organic pollutants. Despite method developments and automation, it is unlikely that laboratory determinations can cope with the pace that new pollutants are identified.

Understanding quantitative structure-property relationships uncertainty in environmental fate modeling.

In cases in which experimental data on chemical-specific input parameters are lacking, chemical regulations allow the use of alternatives to testing, such as in silico predictions based on quantitative structure-property relationships (QSPRs). Such predictions are often given as point estimates; however, little is known about the extent to which uncertainties associated with QSPR predictions contribute to uncertainty in fate assessments. In the present study, QSPR-induced uncertainty in overall persistence (POV ) and long-range transport potential (LRTP) was studied by integrating QSPRs into probabilistic assessments of five polybrominated diphenyl ethers (PBDEs), using the multimedia fate model Simplebox. The uncertainty analysis considered QSPR predictions of the fate input parameters' melting point, water solubility, vapor pressure, organic carbon-water partition coefficient, hydroxyl radical degradation, biodegradation, and photolytic degradation. Uncertainty in POV and LRTP was dominated by the uncertainty in direct photolysis and the biodegradation half-life in water. However, the QSPRs developed specifically for PBDEs had a relatively low contribution to uncertainty. These findings suggest that the reliability of the ranking of PBDEs on the basis of POV and LRTP can be substantially improved by developing better QSPRs to estimate degradation properties. The present study demonstrates the use of uncertainty and sensitivity analyses in nontesting strategies and highlights the need for guidance when compounds fall outside the applicability domain of a QSPR.

The chemical and environmental property space of REACH chemicals.

The European regulation on chemicals, REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals), came into force on 1 June 2007. With pre-registration complete in 2008, data for these substances may provide an overview of the expected chemical space and its characteristics. In this paper, using various in silico computation tools, we evaluate 48782 neutral organic compounds from the list to identify hazardous and safe compounds. Two different classification schemes (modified Verhaar and ECOSAR) identified between 17% and 25% of the compounds as expressing only baseline toxicity (narcosis). A smaller portion could be identified as reactive (19%) or specifically acting (2.7%), while the majority were non-assigned (61%). Overall environmental persistence, bioaccumulation and long-range transport potential were evaluated using structure-activity relationships and a multimedia fugacity-based model. A surprisingly high proportion of compounds (20%), mainly aromatic and halogenated, had a very high estimated persistence (>195 d). The proportion of compounds with a very high estimated bioconcentration or bioaccumulation factor (>5000) was substantially less (6.9%). Finally, a list was compiled of those compounds within the applicability domain of the models used, meeting both persistence and bioaccumulation criteria, and with a long-range transport potential comparable to PCB. This list of 68 potential persistent organic pollutants contained many well-known compounds (all halogenated), but notably also five fluorinated compounds that were not included in the EINECS inventory. This study demonstrates the usability of in silico tools for identification of potentially environmentally hazardous chemicals.

Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling.

Polybrominated diphenyl ethers (PBDEs) are used as flame retardants in textiles, foams and plastics. Highly bioaccumulative with toxic effects including developmental neurotoxicity estrogen and thyroid hormones disruption, they are considered as persistent organic pollutants (POPs) and have been found in human tissues, wildlife and biota worldwide. But only some of them are banned from EU market. For the environmental fate studies of these compounds the bioconcentration factor (BCF) is one of the most important endpoints to start with. We applied quantitative structure-activity relationships techniques to overcome the limited experimental data and avoid more animal testing. The aim of this work was to assess the bioaccumulation of PBDEs by means of QSAR. First, a BCF dataset of specifically conducted experiments was modeled. Then the study was extended by predicting the bioaccumulation and biomagnification factors using some experimental values from the literature. Molecular descriptors were calculated using DRAGON 6. The most relevant ones were selected and resulting models were compared paying attention to the applicability domain.

A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions.

The European REACH legislation accepts the use of non-testing methods, such as QSARs, to inform chemical risk assessment. In this paper, we aim to initiate a discussion on the characterization of predictive uncertainty from QSAR regressions. For the purpose of decision making, we discuss applications from the perspective of applying QSARs to support probabilistic risk assessment. Predictive uncertainty is characterized by a wide variety of methods, ranging from pure expert judgement based on variability in experimental data, through data-driven statistical inference, to the use of probabilistic QSAR models. Model uncertainty is dealt with by assessing confidence in predictions and by building consensus models. The characterization of predictive uncertainty would benefit from a probabilistic formulation of QSAR models (e.g. generalized linear models, conditional density estimators or Bayesian models). This would allow predictive uncertainty to be quantified as probability distributions, such as Bayesian predictive posteriors, and likelihood-based methods to address model uncertainty. QSAR regression models with point estimates as output may be turned into a probabilistic framework without any loss of validity from a chemical point of view. A QSAR model for use in probabilistic risk assessment needs to be validated for its ability to make reliable predictions and to quantify associated uncertainty.

Climate change - An uncertainty factor in risk analysis of contaminated land.

Metals frequently occur at contaminated sites, where their potential toxicity and persistence require risk assessments that consider possible long-term changes. Changes in climate are likely to affect the speciation, mobility, and risks associated with metals. This paper provides an example of how the climate effect can be inserted in a commonly used exposure model, and how the exposure then changes compared to present conditions. The comparison was made for cadmium (Cd) exposure to 4-year-old children at a highly contaminated iron and steel works site in southeastern Sweden. Both deterministic and probabilistic approaches (through probability bounds analysis, PBA) were used in the exposure assessment. Potential climate-sensitive variables were determined by a literature review. Although only six of the total 39 model variables were assumed to be sensitive to a change in climate (groundwater infiltration, hydraulic conductivity, soil moisture, soil:water distribution, and two bioconcentration factors), the total exposure was clearly affected. For example, by altering the climate-sensitive variables in the order of 15% to 20%, the deterministic estimate of exposure increased by 27%. Similarly, the PBA estimate of the reasonable maximum exposure (RME, defined as the upper bound of the 95th percentile) increased by almost 20%. This means that sites where the exposure in present conditions is determined to be slightly below guideline values may in the future exceed these guidelines, and risk management decisions could thus be affected. The PBA, however, showed that there is also a possibility of lower exposure levels, which means that the changes assumed for the climate-sensitive variables increase the total uncertainty in the probabilistic calculations. This highlights the importance of considering climate as a factor in the characterization of input data to exposure assessments at contaminated sites. The variable with the strongest influence on the result was the soil:water distribution coefficient (Kd).

Comparison of theoretical and experimental models for characterizing solvent properties using reversed phase liquid chromatography.

Theoretical and experimental quantitative structure-retention relationships (QSRR) models are useful for characterizing solvent properties and column selectivity in reversed phase liquid chromatography (RPLC). The chromatographic behavior of a model analyte, the herbicide atrazine, in a system derived from nine organic solvents and three chromatographic columns was used for developing QSRR models. Multiple linear regression (MLR) and partial least squares regression (PLSR) were used as statistical approaches. The similarities and differences between linear solvation energy relationships (LSER), and semi-empirical and theoretical molecular models were demonstrated. QSRR models show high predictive power, and can successfully predict retention factor (log k) for new solvents. The models are useful for solvent optimization and reducing time for method development in RPLC. The herbicide atrazine can be readily analyzed at a low level, and all three columns provided good resolution, high-performance and symmetrical peaks. The method is suitable for analysis of atrazine in water samples.

CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals.

Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EU-REACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown.