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

DNA adduct formation in Saccharomyces cerevisiae following exposure to environmental pollutants, as in vivo model for molecular toxicity studies

DNA adduction in the model yeast Saccharomyces cerevisiae was investigated after exposure to the fungicide penconazole and the reference genotoxic compound benzo(a)pyrene, for validating yeasts as a tool for molecular toxicity studies, particularly of environmental pollution. The effect of the toxicants on the yeast's growth kinetics was determined as an indicator of cytotoxicity. Fermentative cultures of S. cerevisiae were exposed to 2 ppm of Penconazole during different phases of growth; while 0.2 and 2 ppm of benzo(a)pyrene were applied to the culture medium before inoculation and on exponential cultures. Exponential respiratory cultures were also exposed to 0.2 ppm of B(a)P for comparison of both metabolisms. Penconazole induced DNA adducts formation in the exponential phase test; DNA adducts showed a peak of 54.93 adducts/109 nucleotides. Benzo(a)pyrene induced the formation of DNA adducts in all the tests carried out; the highest amount of 46.7 adducts/109 nucleotides was obtained in the fermentative cultures after the exponential phase exposure to 0.2 ppm; whereas in the respiratory cultures, 14.6 adducts/109 nucleotides were detected. No cytotoxicity was obtained in any experiment. Our study showed that yeast could be used to analyse DNA adducts as biomarkers of exposure to environmental toxicants.

Quantitative structure-biotransformation relationships of organic micropollutants in aerobic and anaerobic wastewater treatments

Biotransformation is one of the dominant processes to remove organic micropollutants (OMPs) in wastewater treatment. However, studies on the role of molecular structure in determining the biotransformation rates of OMPs are limited. We evaluated the biotransformation of 14 OMPs belonging to different chemical classes under aerobic and anaerobic conditions, and then explored the quantitative structure-biotransformation relationships (QSBRs) of the OMPs based on biotransformation rates using valid molecular structure descriptors (electrical and physicochemical parameters). Pseudo-first-order kinetic modeling was used to fit the biotransformation rate, and only 2 of the 14 OMPs showed that the biotransformation rate constant (kbio) values were higher under anaerobic conditions than aerobic conditions, indicating that aerobic conditions were more favorable for biotransformation of most OMPs. QSBRs infer that the electrophilicity index (ω) is a reliable predictor for OMPs biotransformation under aerobic conditions. ω corresponds to the interaction between OMPs and microbial enzyme active sites, this process is the rate-limiting step of biotransformation. However, under anaerobic conditions the QSBR based on ω was not significant, indicating that specific functional groups may be more critical than electrophilicity. In conclusion, QSBRs can serve as alternative tools for the prediction of the biotransformation of OMPs and provide further insights into the factors that influence biotransformation.

In vivo toxicity of nitroaromatics: A comprehensive quantitative structure-activity relationship study

The toxicity data of 90 nitroaromatic compounds related to their 50% lethal dose concentration for rats (LD50) were analyzed to develop quantitative structure-activity relationship (QSAR) models. Quantum-chemically calculated descriptors together with molecular descriptors generated by DRAGON, PaDEL, and HiT-QSAR software were utilized to build QSAR models. Quality and validity of the models were determined by internal and external validation techniques. The results show that the toxicity of nitroaromatic compounds depends on various factors, such as the number of nitro-groups, the topological state, and the presence of certain structural fragments. The developed models based on the largest (to date) dataset of nitroaromatics in vivo toxicity showed a good predictive ability. The results provide important input that could be applied in a preliminary assessment of nitroaromatic compounds' toxicity to mammals. Environ Toxicol Chem 2017;36:2227-2233. © 2017 SETAC.

Room-temperature and temperature-dependent QSRR modelling for predicting the nitrate radical reaction rate constants of organic chemicals using ensemble learning methods

Experimental determinations of the rate constants of the reaction of NO3 with a large number of organic chemicals are tedious, and time and resource intensive; and the development of computational methods has widely been advocated. In this study, we have developed room-temperature (298 K) and temperature-dependent quantitative structure-reactivity relationship (QSRR) models based on the ensemble learning approaches (decision tree forest (DTF) and decision treeboost (DTB)) for predicting the rate constant of the reaction of NO3 radicals with diverse organic chemicals, under OECD guidelines. Predictive powers of the developed models were established in terms of statistical coefficients. In the test phase, the QSRR models yielded a correlation (r(2)) of >0.94 between experimental and predicted rate constants. The applicability domains of the constructed models were determined. An attempt has been made to provide the mechanistic interpretation of the selected features for QSRR development. The proposed QSRR models outperformed the previous reports, and the temperature-dependent models offered a much wider applicability domain. This is the first report presenting a temperature-dependent QSRR model for predicting the nitrate radical reaction rate constant at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards NO3 radicals in the atmosphere, hence, their persistence and exposure risk assessment.

Toxicity profile of aromatic compounds towards Scenedesmus obliquus: a QSAR study

The parameterization of molecular hydrophobicity and electrophilicity, contributing to the overall toxicity of aromatic compounds, has been the subject of many quantitative structure−activity relationship (QSAR) studies. So far, hydrophobicity has been largely described in terms of the logarithm of the octanol−water partition coefficient (log P) and the molecular electrophilicity in terms of the energy of the lowest unoccupied molecular orbital (ELUMO), the maximum acceptor superdeocalizability (Amax), and the electrophilicity index (ω). Here, we report for the first time the parameterization of these properties in terms of cumulative interplay of multiple descriptors. The toxicity data of 68 compounds were compiled in terms of 50% population growth inhibition (pIGC50) of Scenedesmus obliquus. The comparison of the two QSARs (pIGC50 = 0.175ELUMO + 0.057log P + 0.363ω + 0.019V – 3.292, R2adj = 0.761 and pIGC50 = 0.368ELUMO + 0.146α + 0.258ω + 0.021V − 1.170, R2adj = 0.776) reveals that polarizability (α) is a superior descriptor to log P for parameterization of hydrophobicity, when used in conjunction with ELUMO, ω, and V, for profiling of the toxicity of the test compounds. The overall results indicate that ω and α are better descriptors of electrophilicity and hydrophobicity, respectively, for mapping the toxicity profile of aromatic derivatives towards the target organism.

Evaluation of joint toxicity of nitroaromatic compounds and copper to Photobacterium phosphoreum and QSAR analysis

The individual toxicities of Cu and 11 nitroaromatic compounds to Photobacterium phosphoreum were determined. The toxicity was expressed as the concentrations causing a 50% inhibition of bioluminescence after 15 min exposure (IC(50)). To evaluate the joint effect between the metal ion and the 11 nitroaromatic compounds, the joint toxicity of Cu and 11 nitroaromatic compounds were measured at different Cu concentrations (0.2IC(50), 0.5IC(50) and 0.8IC(50)), respectively. The result shows that the binary joint effect between Cu and nitroaromatic compounds is mainly simple addition at the low Cu concentration (0.2IC(50)). However, an antagonism effect, 55% and 64%, was observed between Cu and 11 nitroaromatic compounds for Cu at medium and high concentrations (0.5IC(50) and 0.8IC(50)). Quantitative structure-activity relationship (QSAR) analysis was performed to study the joint toxicity for the 11 nitroaromatic compounds. The result shows that the toxicity of nitroaromatic compounds is related to descriptors of Connolly solvent-excluded volume (CSEV) and dipolarity/polarizability (S) at low Cu concentration. On the other hand, the toxicity is related to Connolly accessible area (CAA) at medium and high Cu concentrations. The result indicates that different QSAR models on complex mixtures need to be developed to assess the ecological risk in real environments. Using single toxic data to evaluate the toxic effect of mixtures may result in wrong conclusions.

Assessment of quantum-chemical methods for electronic properties and geometry of signaling biomolecules

A reasonable balance between accuracy and feasibility of quantum-chemical methods depends on the complexity of the molecular system and the scientific goals. Six series of indole-, naphthalene-, phenol-, benzoic-, phenoxy-, other auxin-derivatives, and a test set of similar organic molecules have been chosen for an assessment of 13 density functional and semi-empirical molecular orbital methods with respect to electronic and structural properties. The accuracy and precision of HOMO/LUMO calculations are determined by comparison with experimental ionization potentials and electron affinities. Further comparison was performed at atomic level by covariance analysis. The methods KMLYP, MSINDO, and PM3 are precise and accurate for the whole set of molecules. The method AM1 offers comparable accuracy with the exception of electron affinities of indole derivatives, where significant deviations from experiment were observed. Geometrical properties were best reproduced with the semi-empirical method MSINDO.

A Comparative Study of Two Quantum Chemical Descriptors in Predicting Toxicity of Aliphatic Compounds towards Tetrahymena pyriformis

Quantum chemical parameters such as LUMO energy, HOMO energy, ionization energy (I), electron affinity (A), chemical potential (μ), hardness (η) electronegativity (χ), philicity (ωα), and electrophilicity (ω) of a series of aliphatic compounds are calculated at the B3LYP/6-31G(d) level of theory. Quantitative structure-activity relationship (QSAR) models are developed for predicting the toxicity (pIGC50) of 13 classes of aliphatic compounds, including 171 electron acceptors and 81 electron donors, towards Tetrahymena pyriformis. The multiple linear regression modeling of toxicity of these compounds is performed by using the molecular descriptor log P (1-octanol/water partition coefficient) in conjunction with two other quantum chemical descriptors, electrophilicity (ω) and energy of the lowest unoccupied molecular orbital (ELUMO). A comparison is made towards the toxicity predicting the ability of electrophilicity (ω) versus ELUMO as a global chemical reactivity descriptor in addition to log P. The former works marginally better in most cases. There is a slight improvement in the quality of regression by changing the unit of IGC50 from mg/L to molarity and by removing the racemates and the diastereoisomers from the data set.

Externally validated QSPR modelling of VOC tropospheric oxidation by NO3 radicals

The troposphere is the principal recipient of volatile organic chemicals (VOCs) of both anthropogenic and biogenic origin. The persistence of these compounds in the troposphere is an important factor for the evaluation of their fate, and the possible negative effects to the environment and human health. In this study, the tropospheric lifetime of 166 VOCs, in terms of night-time degradation rates with nitrate radical (NO(3)), was modelled by the quantitative structure-property relationships (QSPR) approach. The multiple linear regression method was applied, in combination with the genetic algorithm-variable subset selection procedure, to a variety of theoretical molecular descriptors, calculated by the DRAGON software. The models were developed according to the OECD principles for regulatory acceptance of QSARs, with particular attention to external validation and applicability domain (AD). The external validation was performed on an unbiased external test set or by splitting the available data using self-organized maps or the random by response approach. The best QSPR models presented in this study showed good internal (range of Q(loo)(2): 89-92%) as well as external predictivity (range of Q(ext)(2): 75-89%). The AD of the models was analysed by the leverage approach, and was represented graphically in the Williams graph.

Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces

Novel geometrical chemical descriptors have been derived on the basis of the computational geometry of protein-ligand interfaces and Pauling atomic electronegativities (EN). Delaunay tessellation has been applied to a diverse set of 517 X-ray characterized protein-ligand complexes yielding a unique collection of interfacial nearest neighbor atomic quadruplets for each complex. Each quadruplet composition was characterized by a single descriptor calculated as the sum of the EN values for the four participating atom types. We termed these simple descriptors generated from atomic EN values and derived with the Delaunay Tessellation the ENTess descriptors and used them in the variable selection k-nearest neighbor quantitative structure-binding affinity relationship (QSBR) studies of 264 diverse protein-ligand complexes with known binding constants. Twenty-four complexes with chemically dissimilar ligands were set aside as an independent validation set, and the remaining dataset of 240 complexes was divided into multiple training and test sets. The best models were characterized by the leave-one-out cross-validated correlation coefficient q(2) as high as 0.66 for the training set and the correlation coefficient R(2) as high as 0.83 for the test set. The high predictive power of these models was confirmed independently by applying them to the validation set of 24 complexes yielding R(2) as high as 0.85. We conclude that QSBR models built with the ENTess descriptors can be instrumental for predicting the binding affinity of receptor-ligand complexes.

QSAR studies of paeonol analogues for inhibition of platelet aggregation

Various paeonol analogues were synthesized and tested in vitro as inhibitors of platelet aggregation. Structural properties (or descriptors) of paeonol analogues were calculated and the structure-activity relationships were determined. Several multiple linear and nonlinear regression models and back-propagation neural network model were tested and the latter using relative positive charge, hydration energy, and hydrophilic factor as inputs gave the best data fitting with R2 = 0.89 and q(pre)2 = 0.66. The correlation coefficient between antiplatelet inhibition activity with an interaction energy between the paeonol compounds with COX-1 enzyme is only 0.39.