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.     

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.     

QSAR modeling with the electrotopological state indices: predicting the toxicity of organic chemicals

A quantitative structure-activity relationship model, based on the atom-type electrotopological state (E-state) indices, for the prediction of toxicity to fathead minnow for a diverse set of 140 organic chemicals is presented. Multiple linear regression and artificial neural network techniques were employed in the modeling of experimental toxicity (-logLC(50)) values ranging from 0.85 to 6.09. For the training set of 130 organic compounds a linear regression model with r(2)=0.84 and s=0.36 was obtained with 14 atom-type E-state indices. For the test set of 10 compounds, the corresponding statistics were r(2)=0.83 and s=0.47, respectively. Neural networks gave a significant improvement using the same set of parameters, and the standard deviations were s=0.31 for the training set and s=0.30 for the test set when an artificial neural network with five neurons in the hidden layer was used. The results clearly show that accurate models can be rapidly calculated for the prediction of toxicity for a diverse set of organic chemicals using easily calculated parameters.     

QSAR study on the toxicity of substituted benzenes to the algae (Scenedesmus obliquus).

50% effective inhibition concentration 48h-EC50 of 40 substituted benzenes to the algae (Scenedesmus obliquus) was determined. The energy of the lowest unoccupied molecular orbital (E(LUMO)) was calculated by the quantum chemical method MOPAC6.0-AM1. By using E(LUMO) and the hydrophobicity parameter log K(OW) the quantitative structure-activity relationship model (QSAR) was developed: log1/EC50=0.272 logK(OW) - 0.659E(LUMO) + 2.54, R2 = 0.793, S.E. = 0.316, F = 71.07, n = 40. A series of equations were obtained about the measured EC50 values of different subclasses of compounds. For those compounds containing double -NO2, their toxicity may be related chiefly to the intracellular reduction of -NO2 obtaining electron, while for anilines and phenols, K(OW) contributes most to the QSAR and E(LUMO) very little.     

Comparison of in vitro and in vivo acute fish toxicity in relation to toxicant mode of action

Acute toxicity to fish hepatoma cell line PLHC-1 and to juvenile rainbow trout was examined for 18 plant protection products. The main objective was to explore whether hepatoma cells could be used to predict acute toxicity in fish taking into account the mode of toxic action and compound properties. Acute fish toxicity was determined using the OECD guideline test 203 and compared to predicted baseline LC50 of acute fish toxicity calculated with a quantitative structure-activity relationship (QSAR) derived for guppy fish. Cytotoxicity was determined through the inhibition of neutral red uptake (NR(50)) into lysosomes and compared to predicted baseline cytotoxicity derived for goldfish GFS cells. In general, NR50 values were higher by a factor ranging from 3 to 3000 than the corresponding acute LC50. A weak correlation between NR50 and LC50 values was found (log/log: r2=0.62). Also the lipophilicity (log K(ow)) was not a good predictor for cytotoxicity (r2=0.43) and lethality (r2=0.57) of these pesticides. The neutral red assay is detecting general baseline toxicity only. Comparing LC50 data to QSAR results, the compounds can be classified to act as narcotics or reactive compounds with a specific mode of toxic action in fish. The results indicate that limitation of the neutral red assay in predicting acute fish toxicity. A promising alternative might be the assessment of toxicity in a set of in vitro systems addressing also cell-specific functions which are related to the mode of toxic action of the compound.     

Three-dimensional quantitative structure-activity relationship study for phenylsulfonyl carboxylates using CoMFA and CoMSIA

From both the comparative molecular field analysis (CoMFA) and the comparative molecular similarity indices analysis (CoMSIA), the paper describes two three-dimensional quantitative structure-activity relationship (3D-QSAR) models for the acute toxicity logEC50 (15 min-EC50 in micromoll(-1)) of 56 phenylsulfonyl carboxylates on Photobacterium phosphoreum. Two models yield the leave-one-out cross-validated correlation coefficient q2 values of 0.823 and 0.713, and the conventional correlation coefficient r2 values of 0.958 and 0.933, respectively. The achievement of higher q2 and r2 values of CoMFA model indicates the significance of correlation of steric and electrostatic fields with biological activities. The key features in the CoMFA contour maps are critical to trace the important properties and gain insight into the toxic mechanism of tested compounds. The quality of CoMSIA model is slightly lower than that of CoMFA in terms of q2 and r2 values. Not requiring molecular superposition, CoMSIA is faster than CoMFA in data processing.     

Investigations on the sorption of phenols to dissolved organic matter by a QSAR study

The sorption of various phenols to Aldrich-HA and BSA was investigated by solid phase microextraction (SPME). The Aldrich-HA sorption with log K(DOC)-values between 2 and 3 was determined, whereas the sorption to BSA with log K(DOC)-values between 2 and 6 was much stronger. To enable an estimation of sorption constants a QSAR model was investigated. The linear free energy relationship (LFER) model showed a good correlation between the sorption constants and the log K(OW)-values with correlation coefficients of R = 0.910 and R = 0.878 for Aldrich-HA and BSA, respectively.     

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.     

The aquatic toxicity of anionic surfactants to Daphnia magna--a comparative QSAR study of linear alkylbenzene sulphonates and ester sulphonates

This paper develops quantitative structure activity relationships (QSARs) for the acute aquatic toxicity of the anionic surfactants linear alkylbenzene sulphonates (LAS) and ester sulphonates (ES) to Daphnia magna, the aim being to investigate the modes of action by comparing the QSARs for the two types of surfactant. The generated data for ES have been used to develop a QSAR correlating toxicity with calculated log P values: log(1/EC50)= 0.78 log P+1.37. This equation has an intercept 1.1 log units lower than a QSAR for linear alkylbenzene sulphonates (LAS). The findings suggest that either ES surfactants act by a different mode of action to LAS and other anionic surfactants or the log P calculation method introduces a systematic overestimate when applied to ES.     

Acute toxicity of benzene derivatives to the tadpoles (Rana japonica) and QSAR analyses

Acute lethal toxicity (the negative logarithm of molar concentrations of 12 h acute median lethal, expressed as 12 h-log1/LC50) of 46 benzene derivatives to Rana japonica tadpoles was determined. 1-octanol/water partition coefficient (logKow)-dependent models were developed to study the toxicity of different categories chemicals. In an effort to model all chemicals, response surface analyses and stepwise multiple regression analyses were performed and successful models were obtained. A general and robust QSAR model was achieved with the combined application of variables reflecting hydrophobicity, electric property, and molecular size respectively (12h-log1/LC50 = 0.393logKow - 0.428Elumo + 0.0110Vol. + 1.362 n = 51, r2 = 0.834) using stepwise multiple regression analyses. Because of strong dissociation of carboxyl group greatly decreasing their observed toxicity, using logDow in instead of logKow the quality of the models is greatly improved. The conventional r2 and cross-validation r2(CV) were 0.914 and 0.785, respectively, indicating that QSAR was both internally consistent and highly predictive.     

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.     

Acute toxicity of substituted phenols to Rana japonica tadpoles and mechanism-based quantitative structure-activity relationship (QSAR) study.

Acute 12 h and 24 h lethal toxicity (12 h-LC50 and 24 h-LC50) of 31 substituted phenols to Rana japonica tadpoles was determined. Results indicate that toxicity of phenols to tadpoles varied only slightly with length of exposure and the 12-h test could serve as surrogate of the 24-h test. A mechanism-based quantitative structure-activity relationship (QSAR) method was employed and 1-octanol/water partition coefficient (log K(ow))-dependent models were developed to study different modes of toxic action. Most phenols elicited their response via a polar narcotic mechanism and an excellent logK(ow)-dependent model was obtained. Soft electrophilicity and pro-electrophilicity were observed for some phenols and a good log K(ow)-dependent model was also achieved. Additionally, the significant dissociation of carboxyl on benzoic acid derivatives sharply reduced their toxicity. A statistically robust QSAR model was developed for all studied compounds with the combined application of log K(ow), energy of lowest unoccupied orbital (E(lumo)), heat of formation (HOF) and the first-order path molecular connectivity dices (1chi(p)).     

Mechanism-based quantitative structure-activity relationships for the inhibition of substituted phenols on germination rate of Cucumis sativus

Comparative inhibition activity (GC50) of 42 structurally diverse substituted phenols on seed germination rate of Cucumis sativus was investigated. Quantitative structure-activity relationships (QSARs) were developed by using hydrophobicity (1-octanol/water partition coefficient, logKow) and electrophilicity (the energy of the lowest unoccupied molecule orbital, Eluma) for the toxicity of phenols according to their modes of toxic action. Most phenols elicited their response via a polar narcotic mechanism and a highly significant log Kow-based model was obtained (GC50 = 0.92 log Kow + 1.99, r2 0.84, n = 29). The inclusion of E(lumo) greatly improved the predictive power of the polar narcotic QSAR (GC50 = 0.88 log Kow - 0.30E(lumo) + 1.99, r2 = 0.93, n = 29). pKa proved to be an insignificant influencing factor in this study. Poor correlation with hydrophobicity and strong correlation with electrophilicity were observed for the nine bio-reactive chemicals. Their elevated toxicity was considerably underestimated by the polar narcotic logKow-dependent QSAR. The nine chemicals consist of selected nitro-substituted phenols, hydroquinone, catechol and 2-aminophenol. Their excess toxic potency could be explained by their molecular structure involving in vivo reaction with bio-macromolecules. Strong dissociation of carboxyl group of the four benzoic acid derivatives greatly decreased their observed toxicity. In an effort to model all chemicals including polar narcotics and bio-reactive chemicals, a response-surface analysis with the toxicity, logKow and E(lumo) was performed. This resulted in a highly predictive two-parameter QSAR for most of the chemicals (GC50 = 0. 70 logKow - 0.66E(lumo) + 2.17, r2 = 0.89, n = 36). Catechol and 2,4-dinitrophenol proved to be outliers of this model and their much high toxicity was explained.     

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.