Ecotoxicity prediction using mechanism- and non-mechanism-based QSARs: a preliminary study

In ecotoxicology, mechanism-based quantitative structure-activity relationships (QSARs) are usually developed with higher quality than QSARs without regard to toxicity mechanism. Correctly determining the mechanism of a compound, which is not always easy, is required to use mechanism-based QSARs for toxicity prediction. The mechanism determination step may introduce extra errors in addition to the intrinsic prediction errors of mechanism-based QSARs, thus compromising these QSARs' performance compared with QSARs regardless of mechanism. In this study, the mechanism identification-toxicity prediction (MI-TP) approach was compared with the direct toxicity prediction (DTP) approach using a data set containing phenol toxicity to Tetrahymena pyriformis. A statistical mechanism classification model for mechanism prediction, four mechanism-based QSARs and a single QSAR without discriminating between mechanisms were developed for toxicity prediction. Toxicity of phenols in an external data set was predicted following the MI-TP and DTP approaches. Results indicated that the mechanisms of several phenols in the external test set were incorrectly predicted which led to significant over- or under-estimation of their toxicity. Overall, the MI-TP approach did not yield more accurate toxicity prediction than the DTP approach.     

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.     

Development of QSARs to investigate the bacterial toxicity and biotransformation potential of aromatic heterocylic compounds

A series of aromatic heterocyclic and hydrocarbon compounds were tested for toxicity and biotransformation potential against two contrasting lux-marked whole-cell microbial biosensors. Toxicity was determined by inhibition of light output of a Pseudomonas fluorescens construct that expresses lux constitutively. Biotransformation was tested by increase in light output of P. fluorescens HK44 (pUTK21), which expresses lux when in the presence of a metabolic intermediate (salicylate). The data were then modelled against physical/chemical properties of the compounds tested to see if quantitative structure–activity relationships (QSARs) could be derived. Toxicity was found to be accurately predicted by log K_ow (R²=0.95, Q²=0.88), with the basic (pyridine-ring containing) heterocycles modelled separately. The biotransformation data were best modelled using lowest unoccupied molecular orbital (LUMO) energies (R²=0.90, Q²=0.87).     

An evaluation of global QSAR models for the prediction of the toxicity of phenols to Tetrahymena pyriformis

This study presents an analysis of the ability of a two-parameter response surface, a multiple linear regression and a neural network model to produce global quantitative structure-activity relationships (QSARs) to predict the toxic potency of phenols to Tetrahymena pyriformis. The phenolic toxicity data set analysed is characterised by multiple mechanisms of toxic action. The study aimed to evaluate the confidence that can be applied to the modelling of the differing mechanisms of action. Assessment of confidence was decided in terms of whether the statistics for the global models reflect the ability of the QSARs to model the individual mechanisms of toxic action present in the data set. The results showed that the global statistics only reflected the ability of models to predict the two non-covalent mechanisms (polar narcosis and respiratory uncoupling), with the metabolically transformed and electrophilic mechanism (pre-electrophiles and soft electrophiles) being modelled poorly by all three model building methods. The results confirm the difficulty in modelling electrophilic mechanisms of toxic action. The results also highlight the fact that this poor predictivity is often 'hidden' in good statistical fit of some global models. In particular these results emphasise that for practical predictive purposes the mechanistic applicability domain is required to give confidence to estimated toxicity values.     

Quantitative structure-activity relationships of nitroaromatics toxicity to the algae (Scenedesmus obliguus)

The DFT-B3LYP method, with the basis set 6-311G( * *), was employed to calculate the molecular geometries and electronic structures of 25 nitroaromatics. The acute toxicity (-lgEC(50)) of these compounds to the algae (Scenedesmus obliguus) along with hydrophobicity described by logK(OW), and two quantum chemical parameters-energy of the lowest unoccupied molecular orbital, E(LUMO), and the charge of the nitro group, [ForQ(NO2), were used to establish the quantitative structure-activity relationships (QSARs). For 18 mononitro derivatives, the hydrophobicity parameter logK(OW) could interpret the toxic mechanism successfully. Dinitro aromatic compounds were susceptible to be reduced to aniline for their electrophilic nature. Their toxicity was controlled mainly by electronic factors instead of hydrophobicity. The electronic parameters, E(LUMO) and Q(NO2), were used to yield the following model: -lg EC(50) = 3.746 - 25.053 E(LUMO) + 6.481 Q(NO2) (n=22, R=0.926, SE=0.206, F=56.854, P<0.001). The predicted toxic values using the above equation are in good agreement with the experimental values.     

Quantitative structure-activity relationships for the inhibition toxicity to root elongation of Cucumis sativus of selected phenols and interspecies correlation with Tetrahymena pyriformis

The comparative toxicities of selected phenols to higher plants Cucumis sativus were measured and the negative logarithm molar concentration of the root elongation median inhibition (IRC50) were derived. Quantitative structure-activity relationships (QSARs) were developed to explore the toxicity influencing factors and for predictive purpose. The toxicity data, fell into two classes: polar narcosis and bio-reactive. For polar narcotic phenols, a highly significant two-parameter QSAR based on 1-octanol/water partition coefficient (logKow) and energy of the lowest unoccupied orbital (E(lumo)) was derived (IRC50 = 0.77 log Kow - 0.39E(lumo) + 2.36 n = 22 r2 = 0.89). The five bio-reactive chemicals proved to show elevated toxicity due to their typical substructure involved diverse reactive mechanisms. In an effort to model all chemicals, a robust multiple-variable QSAR combining logKow, E(lumo) and Qmax, the most negative net atomic charge, was developed (IRC50 = 0.65 logKow - 0.72E(lumo) + 0.23Qmax + 2.81 n = 27 r2 = 0.94), indicating that hydrophobicity, electrophilicity and hydrogen bond interaction contribute mainly to the phytotoxicity. The toxicological data was compared with Tetrahymena pyriformis 2-d population growth inhibition toxicity (IGC50) and excellent interspecies correlations were observed both for the polar narcotics and for five reactive chemicals (for polar narcotics: IRC50 = 0.95IGC50 + 1.07 n = 16 r2 = 0.89; for bio-reactive chemicals: IRC50 = 0.98IGC50 + 2.19 n = 5 r2 = 0.97; and for all: IRC50 = 0.93IGC50 + 1.63 n = 21 r2 = 0.87). This suggested that T pyriformis toxicity could serve as a surrogate of C. sativus toxicity for phenols and interspecies correlation also could be established for reactive chemicals.     

有机物的结构──活性定量关系及其在环境化学和环境毒理学中的应用

有机物的结构──活性定量关系及其在环境化学和环境毒理学中的应用王飞越（北京大学城市与环境学系，北京１００８７１）陈雁飞（武汉大学环境科学系）最近几十年来，有机物结构──活性定量关系研究（ＱＳＡＲ，ＱｕａｎｔｉｔａｔｉｖｅＳｔｒｕｃｔｕｒｅ－Ａｃｔｉｖ...     

The influence of quinones and phenols on the triple NAD(H)-dependent enzyme systems

Kinetics of the triple bioluminescent enzyme system: alcohol dehydrogenase--NADH:FMN-oxidoreductase--luciferase in the presence of quinones and phenols has been studied. The correspondence between the bioluminescent kinetic parameters, redox potentials and concentrations of the quinones and phenols has been estimated. The substances have been shown to change bioluminescent kinetics through moving off the NAD+/NADH balance in the enzyme processes. This system is proposed to be used as enzymatic biotest in ecological monitoring.     

Computer-based QSARs for predicting mixture toxicity of benzene and its derivatives

During the past decades, the Quantitative structure-activity relationships (QSARs) have been proven to be reliable tools when little or no empirical data are available in medicinal chemistry, biochemistry, toxicology, and environmental sciences. However, only few studies that quantitatively predict mixtures toxicity have been reported. In this study, the QASR models for the binary mixtures toxicity of 12 benzene and its derivatives, including eight non-polar-narcotic compounds and four polar narcotic compounds were developed, without reference to exact toxicity mechanisms of single compounds. All parameters for the QSAR studies were defined on the basis of quantum mechanical calculations and these parameters were selected by the stepwise procedure. The results of this study provided a simple means of predicting the binary mixtures toxicity from the chemical structure.     

Structure-activity relationships and response-surface analysis of nitroaromatics toxicity to the yeast (Saccharomyces cerevisiae)

Inhibition of growth of the yeast Saccharomyces cerevisiae (Cmiz, the minimum concentration that produced a clear inhibition zone within 12 h) for 24 nitroaromatic compounds was investigated and a quantitative structure-activity relationship (QSAR) developed based on hydrophobicity expressed as the l-octanol/water partition coefficient in logarithm form, log K(ow), electrophilicity based on the energy of the lowest unoccupied orbital (E(lumo)). All nitrobenzene derivatives exhibited enhanced reactive toxicity than baseline. The toxicities of mono-nitrobenzenes and di-nitrobenzenes were elicited by different mechanisms of toxic action. For mono-nitro-derivatives, both significant log K(ow) based and strong E(lumo)-dependent relationships were observed indicating that their toxicities were affected both by the penetration process and the interaction with target sites of interaction. The toxicities of di-nitrobenzenes were greater than mono-nitrobenzenes and no log K(ow)-dependent but highly significant E(lumo)-based relationship was obtained. This suggests that toxicity of di-nitrobenzenes was highly electrophilic and involved mainly their in vivo electrophilic interaction with biomacromolecules. In an effort to model the elevated toxicity of all nitrobenzenes, a response-surface analysis was performed and this resulted in a highly predictive two-variable QSAR without reference to their exact mechanisms (Cmiz = 0.41 log K(ow) - 0.89 E(lumo) - 0.46, r2 = 0.87, Q2 = 0.86, n = 24).     

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.