Toward a class-independent quantitative structure--activity relationship model for uncouplers of oxidative phosphorylation.

A mechanistically based quantitative structure-activity relationship (QSAR) for the uncoupling activity of weak organic acids has been derived. The analysis of earlier experimental studies suggested that the limiting step in the uncoupling process is the rate with which anions can cross the membrane and that this rate is determined by the height of the energy barrier encountered in the hydrophobic membrane core. We use this mechanistic understanding to develop a predictive model for uncoupling. The translocation rate constants of anions correlate well with the free energy difference between the energy well and the energy barrier, Delta G well-barrier,A (-) , in the membrane calculated by a novel approach to describe internal partitioning in the membrane. An existing data set of 21 phenols measured in an in vitro test system specific for uncouplers was extended by 14 highly diverse compounds. A simple regression model based on the experimental membrane-water partition coefficient and Delta G well-barrier,A (-) showed good predictive power and had meaningful regression coefficients. To establish uncoupler QSARs independent of chemical class, it is necessary to calculate the descriptors for the charged species, as the analogous descriptors of the neutral species showed almost no correlation with the translocation rate constants of anions. The substitution of experimental with calculated partition coefficients resulted in a decrease of the model fit. A particular strength of the current model is the accurate calculation of excess toxicity, which makes it a suitable tool for database screening. The applicability domain, limitations of the model, and ideas for future research are critically discussed.

Comparative quantitative structure-activity-activity relationships for toxicity to Tetrahymena pyriformis and Pimephales promelas.

An approach for predicting acute aquatic toxicity, in the form of a quantitative structure-activity-activity relationship (QSAAR), is described. This study assessed relative toxic effects to a fish, Pimephales promelas, and a ciliate, Tetrahymena pyriformis, and attempted to form relationships between them. A good agreement between toxic potencies (R2 = 0.754) was found for a chemically diverse dataset of 364 compounds, when using toxicity to the ciliate as a surrogate to that for fish. This relationship was extended by adding three theoretical structural descriptors of the molecules. The inclusion of these descriptors improved the relationship further (R2 = 0.824). The structural features that were found to improve the extrapolation between the toxicity to the two different species were related to the electron distribution of the carbon skeleton of the toxicant, its hydrogen-bonding ability, and its relative nitrogen content. Such a QSAAR approach provides a potential tool for predicting the toxicities of chemicals for environmental risk assessment and thus for reducing animal tests.

The expanding role of predictive toxicology: an update on the (Q)SAR models for mutagens and carcinogens.

Different regulatory schemes worldwide, and in particular, the preparation for the new REACH (Registration, Evaluation and Authorization of CHemicals) legislation in Europe, increase the reliance on estimation methods for predicting potential chemical hazard. To meet the increased expectations, the availability of valid (Q)SARs becomes a critical issue, especially for endpoints that have complex mechanisms of action, are time-and cost-consuming, and require a large number of animals to test. Here, findings from the survey on (Q)SARs for mutagenicity and carcinogenicity, initiated by the European Chemicals Bureau (ECB) and carried out by the Istituto Superiore di Sanita' are summarized, key aspects are discussed, and a broader view towards future needs and perspectives is given.

Comparison of the applicability domain of a quantitative structure-activity relationship for estrogenicity with a large chemical inventory.

The aim of the present study was to illustrate that it is possible and relatively straightforward to compare the domain of applicability of a quantitative structure-activity relationship (QSAR) model in terms of its physicochemical descriptors with a large inventory of chemicals. A training set of 105 chemicals with data for relative estrogenic gene activation, obtained in a recombinant yeast assay, was used to develop the QSAR. A binary classification model for predicting active versus inactive chemicals was developed using classification tree analysis and two descriptors with a clear physicochemical meaning (octanol-water partition coefficient, or log Kow, and the number of hydrogen bond donors, or n(Hdon)). The model demonstrated a high overall accuracy (90.5%), with a sensitivity of 95.9% and a specificity of 78.1%. The robustness of the model was evaluated using the leave-many-out cross-validation technique, whereas the predictivity was assessed using an artificial external test set composed of 12 compounds. The domain of the QSAR training set was compared with the chemical space covered by the European Inventory of Existing Commercial Chemical Substances (EINECS), as incorporated in the CDB-EC software, in the log Kow / n(Hdon) plane. The results showed that the training set and, therefore, the applicability domain of the QSAR model covers a small part of the physicochemical domain of the inventory, even though a simple method for defining the applicability domain (ranges in the descriptor space) was used. However, a large number of compounds are located within the narrow descriptor window.

Quantitative structure-activity-activity and quantitative structure-activity investigations of human and rodent toxicity.

Acute toxicity in different biological systems, including humans and rodents in vivo, and human and rodent cell lines in vitro, was investigated. The data were taken from the MEIC (Multicentre Evaluation of In Vitro Cytotoxicity) programme. Quantitative structure-activity-activity relationship (QSAAR) models were developed for the in vivo human and rodent toxicity including a combination of toxicity endpoint and structural descriptors as predictor variables. The human peak blood/serum LC(50) concentrations were most strongly related to human liver cell toxicity, while the in vivo oral human lethal doses were most closely related to the in vivo rodent LD(50) values. The QSAARs included structural descriptors encoding electronic/reactivity properties, presence of H-bond donors, compound aromaticity, and size/shape properties. Quantitative structure-activity relationships (QSARs) were derived by using structural descriptors accounting for molecular hydrophobicity, size and shape, and electronic properties. These models have the potential to provide useful insights in the development of non-animal (in vitro and in silico) methods for predicting human and mammalian toxicity.

QSARs for the aquatic toxicity of aromatic aldehydes from Tetrahymena data.

The aim of the study was to develop quantitative structure-activity relationships (QSARs) for a large group of 77 aromatic aldehydes tested for acute toxicity to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The resulting QSARs revealed that the 1-octanol/water partition coefficient (log K(ow)), is the most important descriptor of aldehyde aquatic toxic potency. The model with log K(ow) was improved by adding electronic descriptor (the maximum acceptor superdelocalizability in a molecule--A(max)) based on calculations with the semi-empirical AM1 model. The two descriptors reflect the two main processes responsible for demonstration of acute aquatic toxicity, namely penetration through cell membranes (log K(ow)) and interaction with the biomacromolecules (A(max)) into the cells. Results showed that generally the studied group of aldehydes could be modeled by this simple two-descriptor approach. However, the group of 2- and/or 4-hydroxylated aldehydes demonstrates enhanced toxicity compared to the other aldehydes. Transformation to quinone-like structures is proposed as the explanation for this enhanced potency. The 2- and/or 4-hydroxylated aldehydes are modeled successfully by [log(1/IGC50) = 0.540(0.038) log K(ow) + 8.30(2.88)A(max) - 3.11(0.92), n = 25, R2 = 0.916, R(CV)2 = 0.896, s = 0.141, F = 120], while the other aldehydes are modeled by the relationship [log(1/IGC50) = 0.583 (0.034)log K(ow) + 9.80(2.62)A(max) - 4.04 (0.85), n = 52, R2 = 0.864, R(CV)2 = 0.844, s = 0.203, F = 156], which is similar to the general benzene model.

Structure-toxicity relationships for the effects to Tetrahymena pyriformis of aliphatic, carbonyl-containing, alpha,beta-unsaturated chemicals.

Toxicity data for 82 aliphatic chemicals with an alpha,beta-unsaturated substructure were compiled. Toxicity was assessed in the 2-day Tetrahymena pyriformis population growth impairment assay. Toxic potency [log(IGC50(-1))] for most of these chemicals was in excess of baseline narcosis as quantified by the 1-octanol/water partition coefficient (log K(ow)). The toxicity of the alpha,beta-unsaturated aldehydes was modeled well by log K(ow) in conjunction with the sum of partial charges on the vinylene carbon atoms (Q(C4) + Q(C3)) and the energy of the lowest unoccupied molecular orbital (E(lumo)). These electronic descriptors were also successful at modeling the toxicity of alpha,beta-unsaturated ketones. The toxicity of a range of acrylates was constant within about 0.2 of a log unit. Conversely, the toxicity of methacrylates and esters containing the vinylene group varied considerably and was explained by their hydrophobicity. The comparison of the quantitative structure-activity relationship (QSAR) for the methacrylates and esters with that for non-polar narcosis showed little significant difference and hence suggested that substitution on the carbon-carbon double bond in the methacrylates and vinylene unsaturated esters does not enhance toxicity over that of baseline. Substitution on the carbon-carbon double bond in the alpha,beta-unsaturated aldehydes resulted in toxicity that was similar to that for saturated derivatives. Although an excellent hydrophobicity-dependent QSAR was developed for the esters containing ethynylene group, these compounds are considered to act as Michael-type acceptors. Attempts to combine different groups of Michael-type acceptors into a single QSAR, based on mechanistically derived descriptors, were unsuccessful. Thus, the modeling of the toxicity of the alpha,beta-unsaturated carbonyl domain is currently limited to models for narrow subdomains.

Description of the electronic structure of organic chemicals using semiempirical and ab initio methods for development of toxicological QSARs.

The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K(ow)). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.

Assessment and modeling of the toxicity of organic chemicals to Chlorella vulgaris: development of a novel database.

This study reports a database of toxicity values for 91 compounds assessed in a novel, rapid, and economical 15 min algal toxicity test. The toxicity data were measured using the unicellular green alga Chlorella vulgaris in an assay that determined the disappearance of fluorescein diacetate. The chemicals tested covered a wide range of physicochemical properties and mechanisms of action. Quantitative activity-activity relationships with the toxicity of the chemicals to other species (Tetrahymena pyriformis, Vibrio fischeri, and Pimephales promelas) showed strong relationships, although some differences resulting from different protocols were established. Quantitative structure-activity relationships (QSARs) were determined using linear [multiple linear regression (MLR)] and nonlinear [k-nearest neighbors (KNN)] methods. Three descriptors, accounting for hydrophobicity, electrophilicity, and a function of molecular size corrected for the presence of heteroatoms, were found to be important to model toxicity. The predictivity of MLR was compared to KNN using leave-one-out cross-validation and the simulation of an external test set. MLR demonstrated greater stability in validation. The results of this study showed that method selection in QSAR is task-dependent and it is inappropriate to resort to more complicated but less transparent methods, unless there are clear indications (e.g., inability of MLR to deal with the data set) for the need of such methods.

QSAR analysis of the toxicity of aromatic compounds to Chlorella vulgaris in a novel short-term assay.

The use of alternative toxicity tests and computational prediction models is widely accepted to fill experimental data gaps and to prioritize chemicals for more expensive and time-consuming assessment. A novel short-term toxicity test using the alga Chlorella vulgaris was utilized in this study to produce acute aquatic toxicity data for 65 aromatic compounds. The compounds tested included phenols, anilines, nitrobenzenes, benzaldehydes and other poly-substituted benzenes. The toxicity data were employed in the development of quantitative structure-activity relationships (QSARs). Using multiple regression (MLR) and partial least squares (PLS) analyses, statistically significant, transparent and interpretable QSARs were developed using a small number of physicochemical descriptors. A two-descriptor model was developed using MLR (log(1/EC50)=0.73 log Kow-0.59 Elumo-1.91; n=65, r2=0.84, r2CV=0.82, s=0.43) and a four-descriptor model using PLS (log(1/EC50)=0.40 log Kow-0.23 Elumo+9.84 Amax+0.20 0chiv-5.40; n=65, r2=0.86, q2=0.84, RMSEE=0.40). The latter model was obtained by stepwise elimination of variables from a set of 102 calculated descriptors. Both models were validated successfully by simulating external prediction through the use of complementary subsets. The two factors, which were identified as being critical for the acute algal toxicity of this set of compounds were hydrophobicity and electrophilicity.

Structure-toxicity relationships for aliphatic chemicals evaluated with Tetrahymena pyriformis.

Quantitative structure-activity relationships were developed for the toxicity data of 500 aliphatic chemicals tested in the two-day Tetrahymena pyriformis population growth impairment assay. These chemicals represented a number of structural classes spanning a variety of mechanisms of toxic action including narcoses and electrophilic mechanisms. A series of quantitative structure-toxicity models correlating toxic potency [log(IGC(50)(-1))] with a limited number of mechanistically interpretable descriptors were developed for toxicological domains within the data set. The descriptors included the 1-octanol/water partition coefficient (log K(ow)) (for hydrophobicity) and the energy of the lowest unoccupied molecular orbital (E(lumo)) to quantify electrophilic reactivity. Neutral (nonpolar) narcosis was well modeled by the equation [log(IGC(50)(-1)) = 0.723(0.140) (log K(ow)) - 1.79(0.031); n = 215, r(2) (adj.) = 0.926, s = 0.274, r(2) (pred.) = 0.925]. Chemical classes fitting this domain included saturated alcohols, ketones, nitriles, esters, and sulfur-containing compounds. When the neutral narcotic chemicals were combined with diester narcotics, carboxylic sodium salts, Schiff-based forming aldehydes, electrophilic compounds capable of acting by a S(N)2 mechanism, and proelectrophiles, the model [log(IGC(50)(-1)) = 0.45(0.014) (log K(ow)) - 0.342 (0.035) (E(lumo)) - 1.11(0.05); n = 353, r(2) (adj.) = 0.859, s = 0.353, r(2) (pred.) = 0.857] provided a good fit to the data. The model [log(IGC(50)(-1)) = 0.273(0.018) (log K(ow)) - 0.116(0.056) (E(lumo)) - 0.558(0.054); n = 35, r(2) (adj.) = 0.873, s = 0.141, r(2) (pred.) = 0.838] provided an excellent fit of the data for compounds containing a carboxyl [RC(=O)O] group. The toxicity of aliphatic amines [RCN] was modeled by the equation [log(IGC(50)(-1)) = 0.676(0.048) (log K(ow)) - 1.23(0.08) n = 30, r(2) (adj.) = 0.873, s = 0. 336, r(2) (pred.) = 0.848]. The potency of saturated aliphatic isothiocyanates was a constant (0.0202 mM). Aliphatic chemicals that did not model well by equations involving log K(ow) and E(lumo) included amino alcohols and alpha-haloactivated compounds.

Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis.

Quantitative structure-activity relationships (QSARs) for the toxicity of 200 phenols to the ciliated protozoan Tetrahymena pyriformis, and the validation of the QSARs using a test set of a further 50 compounds, are reported. The phenols are structurally heterogeneous and represent a variety of mechanisms of toxic action including polar narcosis, weak acid respiratory uncoupling, electrophilicity, and those compounds capable of being metabolised or oxidised to quinones. For each compound, a total of 108 physico-chemical descriptors have been calculated. A variety of methods were utilised to develop QSARs and are compared. The response-surface, or two parameter, approach was found to be successful, but only following the removal of compounds known to form quinones. Stepwise regression produced a seven parameter QSAR with good statistical fit, but was less interpretable and transparent than the response-surface. Partial least squares produced a good model for phenolic toxicity following supervised selection of parameters, this, however, was the least transparent of all approaches attempted. In all approaches, a large number of outliers were observed, typically these were compounds capable of being metabolised to quinones. The strengths and weaknesses of each of the approaches to predict the toxicity of the validation (test) set of phenols to T. pyriformis are discussed.

Structure-based classification of antibacterial activity.

The aim of this study was to develop a simple quantitative structure-activity relationship (QSAR) for the classification and prediction of antibacterial activity, so as to enable in silico screening. To this end a database of 661 compounds, classified according to whether they had antibacterial activity, and for which a total of 167 physicochemical and structural descriptors were calculated, was analyzed. To identify descriptors that allowed separation of the two classes (i.e. those compounds with and without antibacterial activity), analysis of variance was utilized and models were developed using linear discriminant and binary logistic regression analyses. Model predictivity was assessed and validated by the random removal of 30% of the compounds to form a test set, for which predictions were made from the model. The results of the analyses indicated that six descriptors, accounting for hydrophobicity and inter- and intramolecular hydrogen bonding, provided excellent separation of the data. Logistic regression analysis was shown to model the data slightly more accurately than discriminant analysis.