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.     

The importance of outlier detection and training set selection for reliable environmental QSAR predictions

Empirical QSAR models are only valid in the domain they were trained and validated. Application of the model to substances outside the domain of the model can lead to grossly erroneous predictions. Partial least squares (PLS) regression provides tools for prediction diagnostics that can be used to decide whether or not a substance is within the model domain, i.e. if the model prediction can be trusted. QSAR models for four different environmental end-points are used to demonstrate the importance of appropriate training set selection and how the reliability of QSAR predictions can be increased by outlier diagnostics. All models showed consistent results; test set prediction errors were very similar in magnitude to training set estimation errors when prediction outlier diagnostics were used to detect and remove outliers in the prediction data. Test set prediction errors for substances classified as outliers were much larger. The difference in the number of outliers between models with a randomly and systematically selected training illustrates well the need of representative training data.     

A novel approach to predict aquatic toxicity from molecular structure

The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity using atom-based non-stochastic and stochastic linear indices. The used dataset consist of 392 benzene derivatives, separated into training and test sets, for which toxicity data to the ciliate Tetrahymena pyriformis were available. Using multiple linear regression, two statistically significant QSAR models were obtained with non-stochastic (R2=0.791 and s=0.344) and stochastic (R2=0.799 and s=0.343) linear indices. A leave-one-out (LOO) cross-validation procedure was carried out achieving values of q2=0.781 (scv=0.348) and q2=0.786 (scv=0.350), respectively. In addition, a validation through an external test set was performed, which yields significant values of Rpred2 of 0.762 and 0.797. A brief study of the influence of the statistical outliers in QSAR's model development was also carried out. Finally, our method was compared with other approaches implemented in the Dragon software achieving better results. The non-stochastic and stochastic linear indices appear to provide an interesting alternative to costly and time-consuming experiments for determining toxicity.     

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.     

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.     

ECOSAR model performance with a large test set of industrial chemicals

The widely used ECOSAR computer programme for QSAR prediction of chemical toxicity towards aquatic organisms was evaluated by using large data sets of industrial chemicals with varying molecular structures. Experimentally derived toxicity data covering acute effects on fish, Daphnia and green algae growth inhibition of in total more than 1,000 randomly selected substances were compared to the prediction results of the ECOSAR programme in order (1) to assess the capability of ECOSAR to correctly classify the chemicals into defined classes of aquatic toxicity according to rules of EU regulation and (2) to determine the number of correct predictions within tolerance factors from 2 to 1,000. Regarding ecotoxicity classification, 65% (fish), 52% (Daphnia) and 49% (algae) of the substances were correctly predicted into the classes "not harmful", "harmful", "toxic" and "very toxic". At all trophic levels about 20% of the chemicals were underestimated in their toxicity. The class of "not harmful" substances (experimental LC/EC(50)>100 mg l(-1)) represents nearly half of the whole data set. The percentages for correct predictions of toxic effects on fish, Daphnia and algae growth inhibition were 69%, 64% and 60%, respectively, when a tolerance factor of 10 was allowed. Focussing on those experimental results which were verified by analytically measured concentrations, the predictability for Daphnia and algae toxicity was improved by approximately three percentage points, whereas for fish no improvement was determined. The calculated correlation coefficients demonstrated poor correlation when the complete data set was taken, but showed good results for some of the ECOSAR chemical classes. The results are discussed in the context of literature data on the performance of ECOSAR and other QSAR models.     

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.     

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.     

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R ²) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

     

Quantification of joint effect for hydrogen bond and development of QSARs for predicting mixture toxicity

A QSAR model is successfully proposed to predict the toxicity effect on Photobacterium phosphoreum by nonpolar-narcotic-chemical mixtures and/or polar-narcotic-chemical mixtures. For nonpolar-narcotic-chemical mixtures and polar-narcotic-chemical mixtures, their corresponding hydrophobicity-based QSAR models are derived from regression analysis. Comparison of these two QSAR models make us believe that it is the joint effect of hydrogen bond in polar-narcotic-chemical mixture that leads to the difference between these two models. Such joint effect of hydrogen bond can be quantified as AMH and BMH by using the different partition coefficients of mixtures in various organic phase/water systems. And the regression analysis results convinced us that the introduction of AMH does improve the quality of the QSAR model with r2=0.948, S.E.=0.166 and F=745.201 at P=0.000 for total 84 mixtures.     

Modelling plume rise and dispersion from pool fires

This paper describes an investigation into the behaviour of smoke plumes from pool fires, and the subsequent generation of empirical models to predict plume rise and dispersion from such a combustion source. Synchronous video records of plumes were taken from a series of small-scale (0.06–0.25m²) outdoor methanol/toluene pool fire experiments, and used to produce sets of images from which plume dimensions could be derived. Three models were used as a basis for the multiple regression analysis of the data set, in order to produce new equations for improved prediction. Actual plume observations from a large (20.7 m×14.2 m) aviation fuel pool fire were also used to test the predictions. The two theoretically based models were found to give a better representation of plume rise and dispersion than the empirical model based on measurements of small-scale fires. It is concluded that theoretical models tested on small-scale fires (heat output ≈70 kW) can be used to predict plume behaviour from much larger combustion sources (heat output ≈70 MW) under near neutral atmospheric conditions.     

Prediction of the adsorption capability onto activated carbon of a large data set of chemicals by local lazy regression method

Accurate quantitative structure–property relationship (QSPR) models based on a large data set containing a total of 3483 organic compounds were developed to predict chemicals’ adsorption capability onto activated carbon in gas phrase. Both global multiple linear regression (MLR) method and local lazy regression (LLR) method were used to develop QSPR models. The results proved that LLR has prediction accuracy 10% higher than that of MLR model. By applying LLR method we can predict the test set (787 compounds) with Q²_ext of 0.900 and root mean square error (RMSE) of 0.129. The accurate model based on this large data set could be useful to predict adsorption property of new compounds since such model covers a highly diverse structural space.     

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.     

Quantitative structure-activity relationship (QSAR) models for polycyclic aromatic hydrocarbons (PAHs) dissipation in rhizosphere based on molecular structure and effect size

Rhizoremediation is a significant form of bioremediation for polycyclic aromatic hydrocarbons (PAHs). This study examined the role of molecular structure in determining the rhizosphere effect on PAHs dissipation. Effect size in meta-analysis was employed as activity dataset for building quantitative structure-activity relationship (QSAR) models and accumulative effect sizes of 16 PAHs were used for validation of these models. Based on the genetic algorithm combined with partial least square regression, models for comprehensive dataset, Poaceae dataset, and Fabaceae dataset were built. The results showed that information indices, calculated as information content of molecules based on the calculation of equivalence classes from the molecular graph, were the most important molecular structural indices for QSAR models of rhizosphere effect on PAHs dissipation. The QSAR model, based on the molecular structure indices and effect size, has potential to be used in studying and predicting the rhizosphere effect of PAHs dissipation.