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.     

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods.     

Application of a computational model for Michael addition reactivity in the prediction of toxicity to Tetrahymena pyriformis

A computational model to predict acute aquatic toxicity to the ciliate Tetrahymena pyriformis has been developed. A general prediction of toxicity can be based on three consecutive steps: 1. Identification of a potential reactive mechanism via structural alerts; 2. Confirmation and quantification of (bio)chemical reactivity; 3. Establishing a relationship between calculated reactivity and toxicity. The method described herein uses a combination of a reactive toxicity (RT) model, including computed kinetic rate constants for adduct formation (log k) via a Michael acceptor mechanism of action, and baseline toxicity (BT), modelled by hydrophobicity (octanol-water partition coefficient). The maximum of the RT and BT values defines acute toxicity for a particular compound. The reactive toxicity model is based on site-specific steric and quantum chemical ground state electronic properties. The performance of the model was examined in terms of predicting the toxicity of 106 potential Michael acceptor compounds covering several classes of compounds (aldehydes, ketones, esters, heterocycles). The advantages of the computational method are described. The method allows for a closer and more transparent mechanistic insight into the molecular initiating events of toxicological endpoints.     

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.     

Discrimination of excess toxicity from narcotic effect: Comparison of toxicity of class-based organic chemicals to Daphnia magna and Tetrahymena pyriformis

The discrimination of excess toxicity from narcotic effect plays a crucial role in the study of modes of toxic action for organic compounds. In this paper, the toxicity data of 758 chemicals to Daphnia magna and 993 chemicals to Tetrahymena pyriformis were used to investigate the excess toxicity. The result showed that mode of toxic action of chemicals is species dependent. The toxic ratio (TR) calculated from baseline model over the experimentally determined values showed that some classes (e.g. alkanes, alcohols, ethers, aldehydes, esters and benzenes) shared same modes of toxic action to both D. magna and T. pyriformis. However, some classes may share different modes of toxic action to T. pyriformis and D. magna (e.g. anilines and their derivatives). For the interspecies comparison, same reference threshold need to be used between species toxicity. The excess toxicity indicates that toxicity enhancement is driven by reactive or specific toxicity. However, not all the reactive compounds exhibit excess toxicity. In theory, the TR threshold should not be related with the experimental uncertainty. The experimental uncertainty only brings the difficulty for discriminating the toxic category of chemicals. The real threshold of excess toxicity which is used to identify baseline from reactive chemicals should be based on the critical concentration difference inside body, rather than critical concentration outside body (i.e. EC₅₀ or IGC₅₀). The experimental bioconcentration factors can be greatly different from predicted bioconcentration factors, resulting in different toxic ratios and leading to mis-classification of toxic category and outliers.     

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.     

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.     

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...     

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.     

Toxicity of 58 substituted anilines and phenols to algae Pseudokirchneriella subcapitata and bacteria Vibrio fischeri: comparison with published data and QSARs

A congeneric set of 58 substituted anilines and phenols was tested using the 72-h algal growth inhibition assay with Pseudokirchneriella subcapitata and 15-min Vibrio fischeri luminescence inhibition assay. The set contained molecules substituted with one, two or three groups chosen from -chloro, -methyl or -ethyl. For 48 compounds there was no REACH-compatible algal toxicity data available before. The experimentally obtained EC50 values (mg L⁻¹) for algae ranged from 1.43 (3,4,5-trichloroaniline) to 197 (phenol) and for V. fischeri from 0.37 (2,3,5-trichlorophenol) to 491 (aniline). Only five of the tested 58 chemicals showed inhibitory effect to algae at concentrations >100 mg L⁻¹, i.e. could be classified as “not harmful”, 32 chemicals as “harmful” (10-100 mg L⁻¹) and 21 as “toxic” (1-10 mg L⁻¹). The occupied para-position tended to increase toxicity whereas most of the ortho-substituted congeners were the least toxic. As a rule, the higher the number of substituents the higher the hydrophobicity and toxicity. However, in case of both assays, the compounds of similar hydrophobicity showed up to 30-fold different toxicities. There were also assay/organism dependent tendencies: phenols were more toxic than anilines in the V. fischeri assay but not in the algal test. The comparison of the experimental toxicity data to the data available from the literature as well as to QSAR predictions showed that toxicity of phenols to algae can be modeled based on hydrophobicity, whereas the toxicity of anilines to algae as well as toxicity of both anilines and phenols to V. fischeri depended on other characteristics in addition to logK_ow.     

The internal barriers of rotation for the 209 polychlorinated biphenyls

The internal barrier of rotation (Erot) was calculated for all 209 polychlorinated biphenyls (PCBs) by using a semi-empirical method, viz. the Austin Model 1 (AMI) Hamiltonian. The difference in total energy between a forced planar state and an optimised twisted structure was defined as Erot. The Erot values were in the range of 8.33 to 483 kj/mol, and were significantly influenced by the number of chlorine atoms inortho position. An additional structural characteristic of the PCBs influencing Erot ofortho substituted congeners was substitution by chlorine atoms in vicinalmeta positions, which is assumed to prevent outward bending ofortho substituents. This so-called buttressing effect contributed with 4 to 31 kj/mol per added chlorine atom. In conclusion, the internal barrier of rotation, calculated for all 209 PCBs, provides an important structure dependent physico-chemical parameter for multivariate modelling of future quantitative structure-activity and structure-property relationships (QSARs/QSPRs).     

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.     

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.