A comparison of DEMETRA individual QSARs with an index for evaluation of uncertainty

Quantitative structure-activity relationships (QSARs) urgently need to be applied in regulatory programs. Many QSAR models can predict the effect of a wide range of substances to different endpoints, particularly in the case of ecotoxicity, but it is difficult to choose the most appropriate model on the basis of the requirements of the application. During the EC-funded project DEMETRA (www.demetra-tox.net) a huge number of QSAR models have been developed for the prediction of different ecotoxicological endpoints. DEMETRA individual models on rainbow trout LC50 after 96 h, water flea LC50 after 48 h and honey bee LD50 after 48 h have been used as a QSAR database to test the advantages of a new index for evaluating model uncertainty. This index takes into consideration the number of outliers (weighted on the total number of compounds) and their root mean square error. Application on the DEMETRA QSAR database indicated that the index can identify the models with the best performance with regard to outliers, and can be used, together with other classical statistical measures (e.g., the squared correlation coefficient), to support the evaluation of QSAR models.     

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure–activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.     

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure-activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.     

3D QSAR studies of dioxins and dioxin-like compounds using CoMFA and CoMSIA

In the present study we have performed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) on structurally diverse ligands of Ah (dioxin) receptor to explore the physico-chemical requirements for binding. All CoMFA and CoMSIA models have given q(2) value of more than 0.5 and r(2) value of more than 0.84. The predictive ability of the models was validated by an external test set, which gave satisfactory predictive r(2) values. Best predictions were obtained with CoMFA model of combined modified training set (q(2) = 0.631, r(2) = 0.900), giving predictive residual value = 0.02 log unit for the test compound. Addition of CoMSIA study has elucidated the role of hydrophobicity and hydrogen bonding along with the effect of steric and electrostatic properties revealed by CoMFA. We have suggested a model comprises of four structurally different compounds, which offers a good predictability for various ligands. Our QSAR model is consistent with all previously established QSAR models with less structurally diverse ligands.     

Comparative study of accessibility of distinctive pesticides

Interactions of micro-contaminants with soil may play a crucial role in their environmental fate and possible harmful effects. Major goals of our investigations were to model the availability of widely used pesticides and characterize adsorption capabilities of distinctive soil types by the accomplishment of extensive comparative studies and application of several extraction methods. Environmental and biological relevance of these examinations is enhanced by the fact that intrinsic features and specific details of pesticide accessibility have not been revealed so far by a comparative approach. Five different experimental methods were assessed for modelling accessibility of five selected pesticides. The applied models for regaining the pesticides showed diverse efficiency in extraction capability in cases of the different soil types (sandy, brown forest and alluvial soils). The amounts of the obtained pesticides were determined by using gas-chromatography coupled to mass spectrometry (GC-MS) and high pressure liquid chromatography coupled to mass spectrometry (HPLC-MS). Accessibility of pesticides was also compared in cases of sterilized and real soil samples in order to estimate the extent of the influence of microflora. Aqueous extraction solvents proved to be suitable for accurate assessment of the accessible amounts of pesticides, as their effectivity was at least as high as that of the applied organic solvents. In our studies pesticide-soil interactions have comprehensively been characterized, and possible influences of environmental factors on the accessibility have also been revealed. Our study might be regarded as a tentative approach to model some significant circumstances playing key roles in pesticides' possible bioavailability.     

定量结构──活性相关研究进展

ＱＳＡＲ方法用于预测环境中化学品的归宿及其毒性与日骤增，本文着重研究ＱＳＡＲ方法在不同领域中的进展和成就，不仅论述了我们关心的ＱＳＡＲ文献进展本身，而且还讨论了它的基础部分。精选ＱＳＡＲ模式在生态毒理学中的应用以及物理化学性质的估算等，     

Algorithms and analytical solutions for rapidly approximating long-term dispersion from line and area sources

Predicting long-term mean pollutant concentrations in the vicinity of airports, roads and other industrial sources are frequently of concern in regulatory and public health contexts. Many emissions are represented geometrically as ground-level line or area sources. Well developed modelling tools such as AERMOD and ADMS are able to model dispersion from finite (i.e. non-point) sources with considerable accuracy, drawing upon an up-to-date understanding of boundary layer behaviour. Due to mathematical difficulties associated with line and area sources, computationally expensive numerical integration schemes have been developed. For example, some models decompose area sources into a large number of line sources orthogonal to the mean wind direction, for which an analytical (Gaussian) solution exists. Models also employ a time-series approach, which involves computing mean pollutant concentrations for every hour over one or more years of meteorological data. This can give rise to computer runtimes of several days for assessment of a site. While this may be acceptable for assessment of a single industrial complex, airport, etc., this level of computational cost precludes national or international policy assessments at the level of detail available with dispersion modelling. In this paper, we extend previous work [S.R.H. Barrett, R.E. Britter, 2008. Development of algorithms and approximations for rapid operational air quality modelling. Atmospheric Environment 42 (2008) 8105–8111] to line and area sources. We introduce approximations which allow for the development of new analytical solutions for long-term mean dispersion from line and area sources, based on hypergeometric functions. We describe how these solutions can be parameterized from a single point source run from an existing advanced dispersion model, thereby accounting for all processes modelled in the more costly algorithms. The parameterization method combined with the analytical solutions for long-term mean dispersion are shown to produce results several orders of magnitude more efficiently with a loss of accuracy small compared to the absolute accuracy of advanced dispersion models near sources. The method can be readily incorporated into existing dispersion models, and may allow for additional computation time to be expended on modelling dispersion processes more accurately in future, rather than on accounting for source geometry.     

The PCB pollution of Lake Iseo (N. Italy) and the role of biomagnification in the pelagic food web

Several models of varying complexity have been used to predict pollutant concentrations in the higher levels of the food web from those in lower levels, but the role of the biomagnification process in aquatic food chains is still controversial. We used the fugacity-based approach to verify the transfer of PCBs through the pelagic food chain of Lake Iseo (N. Italy), sampling several zebra mussel specimens and some fish belonging of different trophic levels. The zebra mussel seems to be a suitable starting species for modelling the bioaccumulation process through the trophic web, not only because its physiological characteristics and population size do not change much with time (as do algae and zooplankton) but also because it takes up toxicants exclusively from the water, as shown by the application of two predictive trophic models commonly used. The data provided by one of those models were in good agreement with our experimental data on fish in Lake Iseo, that show a not negligible uptake from food for the top predator species (pike and perch) with an increase of about three times in comparison with the PCB levels measured in the zebra mussel specimens.     

Modelling skin permeability in risk assessment--the future

The modelling of skin permeability is important for transdermal drug delivery, in the cosmetic industry and for risk assessment attendant on dermal exposure to toxic substances. The two principal methods currently used are quantitative structure-activity relationships (QSARs), used in the main to predict permeability coefficients, and mathematical modelling based on analytical or numerical solutions to the relevant partition and transport equations and used to predict the amount of a substance permeating through the skin. This paper will assess recent progress in this area and suggest what will be needed for future advancements. The considerable effort invested in the development of QSARs during the past decade has resulted in only rather modest progress. Further significant improvement in our ability to predict percutaneous permeability is likely to require the measurement of new data under carefully controlled conditions and its fitting to new QSAR equations. Reliable assessments of risks following dermal exposures will demand new integrated mathematical models that include the variables associated with the exposure and penetration processes as well as the factors that control the subsequent passage of the penetrant into the systemic system.     

First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines

Different regulatory agencies in food and drug administration and environmental protection worldwide are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. Carcinogenicity is a toxicity endpoint of major concern in recent times. Interspecies toxicity correlations may provide a tool for estimating sensitivity towards toxic chemical exposure with known levels of uncertainty for a diversity of wildlife species. In this background, we have developed quantitative interspecies structure-carcinogenicity correlation models for rat and mouse [rodent species according to the Organization for Economic Cooperation and Development (OECD) guidelines] based on the carcinogenic potential of 166 organic chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the OECD principles for the validation of QSAR models. Consensus predictions for carcinogenicity of the individual compounds are presented here for any one species when the data for the other species are available. Informative illustrations of the contributing structural fragments of chemicals which are responsible for specific carcinogenicity endpoints are identified by the developed models. The models have also been used to predict mouse carcinogenicities of 247 organic chemicals (for which rat carcinogenicities are present) and rat carcinogenicities of 150 chemicals (for which mouse carcinogenicities are present). Discriminatory features for rat and mouse carcinogenicity values have also been explored.     

The effect of nitroaromatics' composition on their toxicity in vivo: novel, efficient non-additive 1D QSAR analysis

Novel 1D QSAR approach that allows analysis of non-additive effects of molecular fragments on toxicity has been proposed. Twenty-eight nitroaromatic compounds including some well-known explosives have been chosen for this study. The 50% lethal dose concentration for rats (LD50) was used as the estimation of toxicity in vivo to develop 1D QSAR models on the framework of Simplex representation of molecular structure. The results of 1D QSAR analysis show that even the information about the composition of molecules provides the main trends of toxicity changes. The necessity of consideration of substituents' mutual impacts for the development of adequate QSAR models of nitroaromatics' toxicity was demonstrated. Statistic characteristics for all the developed partial least squares QSAR models, except the additive ones are quite satisfactory (R2=0.81-0.92; Q2=0.64-0.83; R2 test=0.84-0.87). A successful performance of such models is due to their non-additivity i.e. possibility of taking into account the mutual influence of substituents in benzene ring which plays the governing role for toxicity change and could be mediated through the different C-H fragments of the ring. The correspondence between observed and predicted by these models toxicity values is good. This allowing combine advantages of such approaches and develop adequate consensus model that can be used as a toxicity virtual screening tool.     

A protocol to select high quality datasets of ecotoxicity values for pesticides

The key to any QSAR model is the underlying dataset. In order to construct a reliable dataset to develop a QSAR model for pesticide toxicity, we have derived a protocol to critically evaluate the quality of the underlying data. In developing an appropriate protocol that would enable data to be selected in constructing a QSAR, we concentrated on one toxicity end point, the 96 h LC50 from the acute rainbow trout study. This end point is key in pesticide regulation carried out under 91/414/EEC. The dataset used for this exercise was from the US EPA-OPP database.     

Modelling of the migration of trace elements along groundwater flowpaths by using a steady state approach application to the site at El Berrocal (Spain)

The applications of geochemical modelling to natural water systems mostly rely upon the equilibrium assumption. This is in principle justified for deep groundwater systems with long water residence times.In addition, trace element behaviour in these natural systems has been normally modelled by taking into account the precipitation and dissolution of individual trace element phases.Recent geochemical modelling work related to natural analogue systems would indicate that: (1) the equilibrium condition is restricted to a limited number of components in long water residence times and (2) the behaviour of trace metals is very much connected to the major component cycling in the system.There is a need to develop our geochemical modelling capabilities to take these two facts into account in order to be able to predict the behaviour of trace components (radionuclides) in a geological repository.In this work we report on the successful application of steady-state kinetics in conjunction with co-dissolution/co-precipitation approaches to model trace element geochemistry in the natural analogue system at El Berrocal. The evolution of major components of the system (Ca (II), Al (III), CO₃²⁻ and Si) as well the trace elements investigated (U, Ba and Mn) is well reproduced by using the coupling between steady-state kinetics and co-dissolution/co-precipitation approaches.     

Validation of germination rate and root elongation as indicator to assess phytotoxicity with Cucumis sativus

Germination rate and root elongation, as a rapid phytotoxicity test method, possess several advantages, such as sensitivity, simplicity, low cost and suitability for unstable chemicals or samples. These advantages made them suitable for developing a large-scale phytotoxicity database and especially applicable for developing quantitative structure–activity relationship (QSAR) to study mechanisms of phytotoxicity. In this paper, the comparative inhibition of germination rate and root elongation of Cucumis sativus by selected halogen-substituted phenols and anilines were determined. The suitability of germination rate and root elongation as phytotoxicity endpoints was evaluated. Excellent reproducibility and stability of germination rate and root elongation in the control test, relatively greater sensitivity and similar dose–response relations for all tested compounds were observed. These results together with those of a 2-day test were used to demonstrate the suitability of this phytotoxicity test method. A QSAR was developed for the phytotoxicity mode of action of the tested compounds to C. sativus seeds. Models that combined the logarithm of 1-octanol/water partition coefficient (log K_ow) and the energy of the lowest unoccupied molecular orbital (E_lumo) were developed for both germination rate inhibition and root elongation inhibition. The results of these studies indicate that phytotoxicity of substituted phenols and anilines to C. sativus seeds could be explained by a polar narcosis mechanism. This paper will promote the application of germination rate and root elongation method and the development of large-scale phytotoxicity database, which will provide the fundamental data for QSAR and ecological risk assessment of organic pollutants.     

QSARs for organotin compounds on Daphnia magna

A QSAR study has been carried out on several organotin compounds using physical and topological parameters (log P, pKa, ¹x and ¹x^v) and acute toxicity data on Daphnia magna. Equations with significant correlation and high predictive capacity have been found.     

A Protocol to Select High Quality Datasets of Ecotoxicity Values for Pesticides

Abstract

The key to any QSAR model is the underlying dataset. In order to construct a reliable dataset to develop a QSAR model for pesticide toxicity, we have derived a protocol to critically evaluate the quality of the underlying data. In developing an appropriate protocol that would enable data to be selected in constructing a QSAR, we concentrated on one toxicity end point, the 96 h LC50 from the acute rainbow trout study. This end point is key in pesticide regulation carried out under 91/414/EEC. The dataset used for this exercise was from the US EPA-OPP database.