Toxicology of benzyl alcohols: a QSAR analysis

There is an evidence that benzyl alcohols may exhibit toxicity via a radical mechanism. To test this possibility, we studied the toxicity of para substituted benzyl alcohols on rapidly dividing cancer cells (L1210 leukemia). This system has previously found utility in studying the apparent radical toxicity of a variety of phenols. However, no evidence could be found for an electronic effect and the cellular toxicity was associated primarily with hydrophobicity. Comparison of this quantitative structure-activity relationships (QSAR) with others for the reactions of benzyl alcohols in diverse systems provides insight into mechanisms of action. A QSAR for the interaction of benzyl alcohols with protozoa yields an equation that is dependent on both hydrophobicity and acidity of the OH group versus a mixture of bacteria and fungi, the critical dependence on hydrophobicity prevails with a small dependence on a resonance-stabilized, radical mediated electronic effect. The chloramphenicols provide an instructive example, where the radical mediated electronic effect overshadows the hydrophobic contribution to bacterial toxicity. These various QSAR for benzyl alcohols indicate that mechanisms of growth inhibition in vitro vary depending on cell/organism type, the strength of the bond and lability of the hydrogen, and the strength of the initiating radical reagent.     

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.     

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.     

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.     

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.