A novel approach to predict aquatic toxicity from molecular structure

Modelling quantitative structure activity-activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, Daphnia magna, and algae from the VEGA-Hub, as well as Tetrahymena pyriformis and Vibrio fischeri. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.

Fuzzy risk assessment of modified polychlorinated naphthalenes for enhanced degradation

The three-dimensional quantitative structure-activity relationship (3D-QSAR) model is established for polychlorinated naphthalenes (PCNs) using the biological degradability (total score) results to modify CN-56 to design 37 new derivatives with higher degradability (increased by 14.55-38.79%). Furthermore, five new CN-56 derivatives are selected through evaluation of their persistent organic pollutant properties (toxicity, bioconcentration, long-range transport) and practicability (stability, insulativity, flame retardancy) using 3D-QSAR, density functional theory (DFT) and molecular docking methods. Environmental and health-based risk assessments are conducted using the multimedia fugacity model and fuzzy theory for complete screening of the new CN-56 derivatives. Whereas CN-56 is classed as high risk, three new derivatives can be classed as medium risk. The biodegradability mechanism analysis of the PCNs indicates that the electrostatic property is the main factor that affects the degradability, which provides a favorable theoretical reference to obtain environmentally friendly fire retardant and insulating materials.

Environmentally friendly polychlorinated naphthalenes (PCNs) derivatives designed using 3D-QSAR and screened using molecular docking, density functional theory and health-based risk assessment

A complete design and screening system for environmental-friendly polychlorinated naphthalene (PCN) derivatives was established through three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, density functional theory (DFT) methods and health-based risk assessment based on dynamic multimedia fugacity model. Two types of 3D-QSAR models were established for PCNs using the experimental biological toxicity (logEC₅₀) of 14 PCNs to carry out a modification to lower the logEC₅₀ of CN-70. Consequently, 67 new monosubstituted and disubstituted derivatives with a lower biological toxicity than CN-70 were designed. Furthermore, 21 new CN-70 derivatives were selected through the evaluation of their persistent organic pollutant properties (biological toxicity, bio-concentration, long-range transport potential, biodegradability) and practicability (stability, insulativity, flame retardancy) using 3D-QSAR, molecular docking and DFT methods. Finally, the non-carcinogenic and carcinogenic risks of 19 new CN-70 derivatives in different exposure pathways were reduced, and 5 derivatives with a significant decrease both in biological toxicity (amplitude reduction: 12.73%-32.51%) and risk (amplitude reduction: 32.18%-59.19%) were selected as environmental-friendly PCN derivatives, which had been screened using the health-based risk assessment system associated with dynamic multimedia fugacity model. This study provides a theoretical basis for the design of environmental-friendly flame retardants and insulating materials.

Use of the index of ideality of correlation to improve models of eco-toxicity

Persistent organic pollutants are compounds used for various everyday purposes, such as personal care products, food, pesticides, and pharmaceuticals. Decomposition of considerable part of the above pollutants is a long-time process. Under such circumstances, estimation of toxicity for large arrays of organic substances corresponding to the above category of pollutants is a necessary component of theoretical chemistry. The CORAL software is a tool to establish quantitative structure-activity relationships (QSARs). The index of ideality of correlation (IIC) was suggested as a criterion of predictive potential of QSAR. The statistical quality of models for eco-toxicity of organic pollutants, which are built up, with use of the IIC is better than statistical quality of models, which are built up without use of data on the IIC.

Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors

Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50)^-1). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure-toxicity relationships; the best model shows values of Q²=0.830 and R²=0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives.

In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach

Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for R_training² and R_test², respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for R_training² and R_test², respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (R_training² and R_test² were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615.

Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database

In this article, the modeling of inhibitory grown activity against Tetrahymena pyriformis is described. The 0-2D Dragon descriptors based on structural aspects to gain some knowledge of factors influencing aquatic toxicity are mainly used. Besides, it is done by some enlarged data of phenol derivatives described for the first time and composed of 358 chemicals. It overcomes the previous datasets with about one hundred compounds. Moreover, the results of the model evaluation by the parameters in the training, prediction and validation give adequate results comparable with those of the previous works. The more influential descriptors included in the model are: X3A, MWC02, MWC10 and piPC03 with positive contributions to the dependent variable; and MWC09, piPC02 and TPC with negative contributions. In a next step, a median-size database of nearly 8000 phenolic compounds extracted from ChEMBL was evaluated with the quantitative-structure toxicity relationship (QSTR) model developed providing some clues (SARs) for identification of ecotoxicological compounds. The outcome of this report is very useful to screen chemical databases for finding the compounds responsible of aquatic contamination in the biomarker used in the current work.

The mixture toxicity of environmental contaminants containing sulfonamides and other antibiotics in Escherichia coli: Differences in both the special target proteins of individual chemicals and their effective combined concentration

Organisms in the environment are exposed to mixtures of multiple contaminants, leading to serious environmental harm. These mixtures pose an ecological risk and have attracted an increasing amount of attention; however there has been little in-depth research the toxicity of mixtures, such as antibiotics. To determine how different mixtures of antibiotics affect organisms, the individual and mixture toxicity of sulfonamides and several antibiotics were determined using Escherichia coli as a target organism in our study. The results show that additive effects occur between sulfonamides and quinolones or with a portion of β-lactams, synergistic effects appear between sulfonamides and their potentiators or cefotaxime sodium, and antagonistic effects arise between sulfonamides and tetracyclines or penicillin V potassium salt. In addition, the toxicity mechanism of binary mixtures is further discussed and the results reveal that the joint effect differences depend not only the target proteins of individual chemicals but also on their effective combined concentration based on the approach of Quantitative Structure Activity Relationships (QSARs) and molecular docking. This study introduces the concept of the "effective concentration" to provide insight into understanding the mechanism of binary mixtures, which will be beneficial for evaluating the ecological risk of antibiotics.

Discrimination of excess toxicity from narcotic effect: influence of species sensitivity and bioconcentration on the classification of modes of action

The toxicity data of 2624 chemicals to fish, Daphniamagna, Tetrahymenapyriformis and Vibriofischeri were used to investigate the effects of species sensitivity and bioconcentration on excess toxicity. The results showed that 47 chemical classes were identified as having the same modes of action (MOAs) to all four species, but more than half of the classes were identified as having different MOAs. Difference in chemical MOAs is one of the reasons resulting in the difference in toxic effect to these four species. Other important reasons are the difference in sensitivity and bioconcentration of species. Among the four species, V. fischeri has the most compounds identified as reactive MOA. This may be due to some compounds can be easily absorbed into the bacteria, react with the DNA or proteins, disrupt the normal function of the cell and exhibit significantly greater toxicity to the bacteria. On the other hand, the skin and lipid content of aqueous organisms can strongly inhibit the bio-uptake for some reactive compounds, resulting in a less toxic effect than expected. D. magna is the most sensitive species and T. pyriformis is the least sensitive species of the four species. For a comparison of interspecies toxicity, we need to use the same reference threshold of excess toxicity. However, some reactive compounds may be identified as baseline or less inert compounds for low sensitive species from the threshold developed from high sensitive species. The difference in the discrimination of excess toxicity to different species is not only because of the difference in MOAs for some compounds, but also due to the difference in sensitivity and bioconcentration.

Comparison of global and mode of action-based models for aquatic toxicity

The ability to estimate aquatic toxicity is a critical need for ecological risk assessment and chemical regulation. The consensus in the literature is that mode of action (MOA) based toxicity models yield the most toxicologically meaningful and, theoretically, the most accurate results. In this study, a two-step prediction methodology was developed to estimate acute aquatic toxicity from molecular structure. In the first step, one-against-the-rest linear discriminant analysis (LDA) models were used to predict the MOA. The LDA models were able to predict the MOA with 85.8-88.8% accuracy for broad and specific MOAs, respectively. In the second step, a multiple linear regression (MLR) model corresponding to the predicted MOA was used to predict the acute aquatic toxicity value. The MOA-based approach was found to yield similar external prediction accuracy (r(2) = 0.529-0.632) to a single global MLR model (r(2) = 0.551-0.562) fit to the entire training set. Overall, the global hierarchical clustering approach yielded a higher combination of accuracy and prediction coverage (r(2) = 0.572, coverage = 99.3%) than the other approaches. Utilizing multiple two-dimensional chemical descriptors in MLR models yielded comparable results to using only the octanol-water partition coefficient (log K(ow)).

Predicting aquatic toxicities of benzene derivatives in multiple test species using local, global and interspecies QSTR modeling approaches

A flow diagram showing QSTR modeling strategy for aquatic toxicity prediction of benzene derivatives in multiple test species.

Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions

Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and biological/chemical properties. However, the potential toxic effects of nanoparticles to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanoparticles against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle-nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results of this study thus provide a single valuable tool toward an efficient prediction of the ecotoxicity of nanoparticles under multiple experimental conditions.

Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach

Nowadays, the interest in the search for new nanomaterials with improved electrical, optical, catalytic and biological properties has increased. Despite the potential benefits that can be gathered from the use of nanoparticles, only little attention has been paid to their possible toxic effects that may affect human health. In this context, several assays have been carried out to evaluate the cytotoxicity of nanoparticles in mammalian cells. Owing to the cost in both resources and time involved in such toxicological assays, there has been a considerable increase in the interest towards alternative computational methods, like the application of quantitative structure-activity/toxicity relationship (QSAR/QSTR) models for risk assessment of nanoparticles. However, most QSAR/QSTR models developed so far have predicted cytotoxicity against only one cell line, and they did not provide information regarding the influence of important factors rather than composition or size. This work reports a QSTR-perturbation model aiming at simultaneously predicting the cytotoxicity of different nanoparticles against several mammalian cell lines, and also considering different times of exposure of the cell lines, as well as the chemical composition of nanoparticles, size, conditions under which the size was measured, and shape. The derived QSTR-perturbation model, using a dataset of 1681 cases (nanoparticle-nanoparticle pairs), exhibited an accuracy higher than 93% for both training and prediction sets. In order to demonstrate the practical applicability of our model, the cytotoxicity of different silica (SiO2), nickel (Ni), and nickel(ii) oxide (NiO) nanoparticles were predicted and found to be in very good agreement with experimental reports. To the best of our knowledge, this is the first attempt to simultaneously predict the cytotoxicity of nanoparticles under multiple experimental conditions by applying a single unique QSTR model.

3D-QSAR studies on the toxicity of substituted benzenes to Tetrahymena pyriformis: CoMFA, CoMSIA and VolSurf approaches

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis were performed on the toxicity of a large set of substituted benzenes toward ciliate Tetrahymena pyriformis. The 3D-QSAR studies were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and VolSurf techniques. The optimal CoMFA and CoMSIA models obtained from the training set were all statistically significant with correlation coefficients (R(2)) greater than 0.79 and absolute error less than 0.33 in log units. The predictive ability of the models was externally evaluated through the prediction of a test set (20 percent of the whole data set) that were not included in the training set. A simple and fairly good predictive linear model based on VolSurf descriptors was also developed that showed an adequate prediction power of the toxicity (pIGC50) of substituted benzenes. Validation, reliability and robustness of models were also evaluated by leave-one-out, leave-four-out, bootstrapping and progressive scrambling approaches. The results confirmed that in addition to hydrophobic effects, electrostatic and H-bonding interactions also play important roles in the toxicity of substituted benzenes. The information obtained from CoMFA and CoMSIA 3-D contour maps could be useful to explain the toxicity mechanism of substituted benzenes.

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.

Using molecular docking-based binding energy to predict toxicity of binary mixture with different binding sites

The flood of chemical substances in the environment result in the complexity of chemical mixtures, and one of the reasons for complexity is that their individual chemicals bind to different binding sites on different (or same) target proteins within the organism. A general approaches therefore are proposed in this study to predict the toxicity of chemical mixtures with different binding sites by using molecular docking-based binding energy (Ebinding). Aldehydes and cyanogenic toxicants were selected as the example of chemical mixtures with same binding site. Triazines and urea herbicide were selected as the example of chemical mixtures with different binding sites but on same target protein. Sulfonamides and trimethoprim toxicants were selected as the example of chemical mixtures with different target proteins. Although these chemical mixtures bind to their binding sites by different ways, there is a general relationship between their binary mixture toxicity (EC50M) and their corresponding Ebinding of individual chemicals and logKow(mix). By using the Ebinding to describe how the individual chemicals work in the different binding sites, the approach may provide a general and simply model to predict mixture toxicity to microorganism.

Comparative study to predict toxic modes of action of phenols from molecular structures

Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.

Quantitative structure activity relationships (QSAR) for binary mixtures at non-equitoxic ratios based on toxic ratios-effects curves

The present study proposed a QSAR model to predict joint effects at non-equitoxic ratios for binary mixtures containing reactive toxicants, cyanogenic compounds and aldehydes. Toxicity of single and binary mixtures was measured by quantifying the decrease in light emission from the Photobacterium phosphoreum for 15 min. The joint effects of binary mixtures (TU sum) can thus be obtained. The results showed that the relationships between toxic ratios of the individual chemicals and their joint effects can be described by normal distribution function. Based on normal distribution equations, the joint effects of binary mixtures at non-equitoxic ratios ( [Formula: see text]) can be predicted quantitatively using the joint effects at equitoxic ratios ( [Formula: see text]). Combined with a QSAR model of [Formula: see text]in our previous work, a novel QSAR model can be proposed to predict the joint effects of mixtures at non-equitoxic ratios ( [Formula: see text]). The proposed model has been validated using additional mixtures other than the one used for the development of the model. Predicted and observed results were similar (p>0.05). This study provides an approach to the prediction of joint effects for binary mixtures at non-equitoxic ratios.

Comparison of Tetrahymena pyriformis toxicity based on hydrophobicity, polarity, ionization and reactivity of class-based compounds

A toxicity data set containing the toxicities of 970 hydrophobic, polar and ionizable, nitro substituted and α,β-unsaturated compounds to Tetrahymena pyriformis was classified into different groups based on the structure and substituted functional groups. Polar, ionizable and reactive compounds exhibit greater toxicity as compared with the non-polar hydrophobic compounds. Step-by-step analysis was carried out between the toxicity and descriptors representing hydrophobicity, polarity/polarizability, ionization and reactivity of compounds. Significant relationships were developed between the toxicity and these descriptors for the compounds. The models developed are simple, interpretable and transparent, using a small number of descriptors that may reflect the interactions of chemicals with the biological macromolecules at the target sites. Hydrophobic parameter log P reflects bio-uptake process compounds. Polarity/polarizability descriptor S reflects the interaction of hydrophilic residues of polar chemicals with biological macromolecules. The fractions of ionized (F (i)) and neutral (F (0)) forms calculated from pK (a) reflect the interactions of ionizable compounds with the macromolecules and effect of ionization of ionizable compounds on the bio-uptake process, respectively. A successful single model was developed by using the descriptors log P, S, F (i) and log F (0) for non-polar, polar and ionizable compounds.

Atomic charges of individual reactive chemicals in binary mixtures determine their joint effects: an example of cyanogenic toxicants and aldehydes

Environmental contaminants are usually encountered as mixtures, and many of these mixtures yield synergistic or antagonistic effects attributable to an intracellular chemical reaction that pose a potential threat on ecological systems. However, how atomic charges of individual chemicals determine their intracellular chemical reactions, and then determine the joint effects for mixtures containing reactive toxicants, is not well understood. To address this issue, the joint effects between cyanogenic toxicants and aldehydes on Photobacterium phosphoreum were observed in the present study. Their toxicological joint effects differed from one another. This difference is inherently related to the two atomic charges of the individual chemicals: the oxygen charge of -CHO (O(aldehyde toxicant)) in aldehyde toxicants and the carbon-atom charge of a carbon chain in the cyanogenic toxicant (C(cyanogenic toxicant)). Based on these two atomic charges, the following QSAR (quantitative structure-activity relationship) model was proposed: When (O(aldehyde toxicant) -C(cyanogenic toxicant) )> -0.125, the joint effect of equitoxic binary mixtures at median inhibition (TU, the sum of toxic units) can be calculated as TU = 1.00 ± 0.20; when (O(aldehyde toxicant) -C(cyanogenic toxicant) ) ≤ -0.125, the joint effect can be calculated using TU = - 27.6 x O (aldehyde toxicant) - 5.22 x C (cyanogenic toxicant) - 6.97 (n = 40, r = 0.887, SE = 0.195, F = 140, p < 0.001, q(2) (Loo) = 0.748; SE is the standard error of the regression, F is the F test statistic). The result provides insight into the relationship between the atomic charges and the joint effects for mixtures containing cyanogenic toxicants and aldehydes. This demonstrates that the essence of the joint effects resulting from intracellular chemical reactions depends on the atomic charges of individual chemicals. The present study provides a possible approach for the development of a QSAR model for mixtures containing reactive toxicants based on the atomic charges.

QSTR with extended topochemical atom (ETA) indices. 15. Development of predictive models for toxicity of organic chemicals against fathead minnow using second-generation ETA indices

Modern industrialisation has led to the production of millions of toxic chemicals having hazardous effects on the ecosystem. It is impracticable to determine the toxic potential of a large number of chemicals in animal models, making the use of quantitative structure-toxicity relationship (QSTR) models an alternative strategy for toxicity prediction. Recently we introduced a set of second-generation extended topochemical atom (ETA) indices for predictive modelling. Here we have developed predictive toxicity models on a large dataset of 459 diverse chemicals against fathead minnow (Pimephales promelas) using the second-generation ETA indices. These descriptors can be easily calculated from two-dimensional molecular representation without the need of time-consuming conformational analysis and alignment, making the developed models easily reproducible. Considering the importance of hydrophobicity for toxicity prediction, AlogP98 was used as an additional predictor in all the models, which were validated rigorously using multiple strategies. The ETA models were comparable in predictability to those involving various non-ETA topological parameters and those previously reported using various descriptors including computationally demanding quantum-chemical ones.

Interspecies correlations of toxicity to eight aquatic organisms: theoretical considerations

Interspecies correlations allow the prediction of toxicity to a number of other species. However, little attention has been paid to the theoretical considerations of the interspecies relationship based on the differences of bio-uptake and toxic mechanism between species. This study examines the interspecies correlations of toxicity between species of Vibrio fischeri, river bacteria, algae, Daphnia magna, carp, Tetrahymena pyriformis, fathead minnow and guppy based on the theoretical background. The results show that there are good interspecies correlations between marine bacterium and fresh water bacteria or fish and fish. It is suggested that compounds share the same bio-uptake and toxic mechanism of action between the species. On the other hand, poor interspecies relationships were found between toxicities to algae and T. pyriformis or D. magna. It is suggested that compounds have different toxic mechanisms of action between these species. Interspecies relationships can be improved by inclusion of the octanol/water partition coefficient or the energy of the lowest unoccupied molecular orbital. They reflect the difference of bio-uptake or toxic mechanism of action between species for organic compounds. Benzoic acids show very different toxicity contributions to the three species, V. fischeri, D. magna and carp. They can be easily absorbed into the unicellular bacteria, V. fischeri. On the contrary, the skin and lipid content of multicellular organisms, such as D. magna and fish, can strongly inhibit the bio-uptake for ionizable compounds, which results in the different toxic effect between V. fischeri and D. magna or carp. Good correlation coefficients were observed between toxicities to V. fischeri and D. magna or fishes by inclusion of hydrophobic and ionization parameters. V. fischeri or D. magna can serve as a surrogate of fish toxicity for hydrophobic and ionizable compounds studied. Toxic mechanisms of action are discussed based on the theoretical background of the interspecies correlation.

Toxicity of organic chemicals to Tetrahymena pyriformis: effect of polarity and ionization on toxicity

A large toxicity data set containing the toxicities of 250 phenols and 252 aliphatic compounds to Tetrahymena pyriformis was classified into different groups based on the structure and substituted functional groups. QSAR analysis was performed between the toxicity and calculated descriptors, expressed as hydrophobicity, polarity and ionization. Through an analysis of these class-based compounds, significant relationships were developed between the toxicity and hydrophobicity for non-polar and polar narcotic compounds. A single model for both non-polar and polar narcotics was developed by inclusion of a polar descriptor as well as the hydrophobic parameter logP. The highly hydrophobic polar narcotics can be treated as non-polar narcotics because their polar functional group(s) makes a relatively small contribution as compared to their hydrophobicity. A cut-off to classify the polar narcotics is difficult because polarity of a chemical not only depends on one or two functional groups (i.e. amino- or hydroxyl-) substituted on the compound, but also on the overall hydrophobicity of the compound. The toxicity increases with increasing the ionization by increasing the interaction between ionisable compounds and macromolecules at the target sites. However, the toxicity decreases with increasing the ionization by decreasing the bio-uptake for extremely ionisable compounds. A significant QSAR equation has been developed between the toxicity to T. pyriformis and the descriptors of hydrophobic, polarity/polarizability and ionization parameters for 457 compounds (R(2)=0.87). These compounds contain non-polar, polar and reactive compounds, and some of them are extremely ionisable. The models developed are simple, interpretable and transparent, using a small number of descriptors.

Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species

There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.

Computational discovery of novel trypanosomicidal drug-like chemicals by using bond-based non-stochastic and stochastic quadratic maps and linear discriminant analysis

Herein we present results of a quantitative structure-activity relationship (QSAR) studies to classify and design, in a rational way, new antitrypanosomal compounds by using non-stochastic and stochastic bond-based quadratic indices. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop QSAR models based on linear discriminant analysis (LDA). Non-stochastic model correctly classifies more than 93% and 95% of chemicals in both training and external prediction groups, respectively. On the other hand, the stochastic model shows an accuracy of about the 87% for both series. As an experiment of virtual lead generation, the present approach is finally satisfactorily applied to the virtual evaluation of 9 already synthesized in house compounds. The in vitro antitrypanosomal activity of this series against epimastigote forms of Trypanosoma cruzi is assayed. The model is able to predict correctly the behaviour for the majority of these compounds. Four compounds (FER16, FER32, FER33 and FER 132) showed more than 70% of epimastigote inhibition at a concentration of 100 microg/mL (86.74%, 78.12%, 88.85% and 72.10%, respectively) and two of these chemicals, FER16 (78.22% of AE) and FER33 (81.31% of AE), also showed good activity at a concentration of 10 microg/mL. At the same concentration, compound FER16 showed lower value of cytotoxicity (15.44%), and compound FER33 showed very low value of 1.37%. Taking into account all these results, we can say that these three compounds can be optimized in forthcoming works, but we consider that compound FER33 is the best candidate. Even though none of them resulted more active than Nifurtimox, the current results constitute a step forward in the search for efficient ways to discover new lead antitrypanosomals.

Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species

The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.