Molecular orbital parameters as predictors of skin sensitization potential of halo- and pseudohalobenzenes acting as SNAr electrophiles

Spectroscopic and molecular modeling investigation on inhibition effect of nitroaromatic compounds on acetylcholinesterase activity

Nitroaromatic compounds (NACs) are widely distributed in the environment and are considered toxic or carcinogenic. However, little attention has been paid to the binding interactions between NACs and biomacromolecules (e.g., proteins). Here we investigated the effects of three model NACs, nitrobenzene (NB), 1,3-dinitrobenzene (DNB), and 1,3,5-trinitrobenzene (TNB), on the activity of acetylcholinesterase (AChE). The presence of NACs (up to 0.5 mM) effectively suppressed the AChE-catalyzed hydrolysis of acetylthiocholine iodide, with the suppression effect increasing with the nitro-group substitution (TNB > DNB > NB). Consistently, the UV absorption of AChE at 206 nm arising from the skeleton structure decreased by the addition NACs, and the decrease exhibited the same compound sequence, reflecting the perturbing interactions with the skeleton enzyme structure. However, no changes were made on the secondary structure of AChE, as evidenced by the circular dichroism analysis. The fluorescence quenching analysis of AChE demonstrated that NB and DNB interacted with both tryptophan (Trp) and tyrosine (Tyr) residues, whereas TNB interacted only with Trp. The UV absorption and fluorescence quenching analyses both reflected that the interactions with the non-skeleton aromatic amino acids were weak. ¹H NMR analysis confirmed the strong π-π coupling interactions between TNB and model Trp. Molecular simulation indicated that the DNB or TNB molecule was sandwiched between Trp84 and Phe330 at the catalytic site via π-π coupling interactions. The findings highlight the importance of specific interactions of NACs with proteins to cause them to malfunction.

A simple approach for assessment of toxicity of nitroaromatic compounds without using complex descriptors and computer codes

A simple approach is introduced to assess the toxicity of nitroaromatic compounds in terms of an oral LD₅₀ dose (50% lethal dose) for rats. Most of the presented Quantitative Structure-Activity Relationship (QSAR) models for prediction of in vivo toxicity of nitroaromatics are calculated by quantum computing descriptors which are more difficult to interpret and apply, while the new model requires only the molecular structure of a desirable nitroaromatic compound. The novel model is based on the constitutional descriptors, such as the number of oxygen, sulphur, phosphorous and molecular fragments. Experimental data of 90 nitroaromatics are used to derive and test the new model as the logarithm of LD₅₀ values, i.e. -log (LD₅₀). Although it is based on only simple structural parameters, the reliability of the new model is also higher than the complex QSAR model because the values of the root-mean-square deviation (RMSD) of -log (LD₅₀) for the new and the outputs of the latest QSAR method are 0.342 and 0.377, respectively.

An Integrated Decision-tree Testing Strategy for Skin Sensitisation with Respect to the Requirements of the EU REACH Legislation

This report presents some of the results of a joint research project, sponsored by Defra and conducted by FRAME and Liverpool John Moores University, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This report focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for skin sensitisation testing. The manuscript reviews in vitro tests based on protein-ligand binding, dendritic/Langerhans cells and T-lymphocyte activation, and also the QSAR models and expert systems available for this endpoint. These tests are then incorporated into an integrated, decision-tree testing strategy, which also includes the Local Lymph Node Assay (in its original and new reduced protocols) and the traditional guinea-pig tests (which should only be used as a last resort). The aim of the strategy is to minimise the use of animals in testing for skin sensitisation, while satisfying the scientific and logistical demands of the EU REACH legislation.

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.

A quantum chemical based toxicity study of estimated reduction potential and hydrophobicity in series of nitroaromatic compounds

Nitroaromatic compounds and the products of their degradation are toxic to bacteria, cells and animals. Various studies have been carried out to better understand the mechanism of toxicity of aromatic nitrocompounds and their relationship to humans and the environment. Recent data relate cytotoxicity of nitroaromatic compounds to their single- or two-electron enzymatic reduction. However, mechanisms of animal toxicity could be more complex. This work investigates the estimated reduction and oxidation potentials of 34 nitroaromatic compounds using quantum chemical approaches. All geometries were optimized with density functional theory (DFT) using the solvation model based on density (SMD) and polarizable continuum model (PCM) solvent model protocols. Quantitative structure-activity/property (QSAR/QSPR) models were developed using descriptors obtained from quantum chemical optimizations as well as the DRAGON software program. The QSAR/QSPR equations developed consist of two to four descriptors. Correlations have been identified between electron affinity (E_LUMO) and hydrophobicity (log P).

The kinetics and QSAR of abiotic reduction of mononitro aromatic compounds catalyzed by activated carbon

The kinetics of abiotic reduction of mono-nitro aromatic compounds (mono-NACs) catalyzed by activated carbon (AC) in an anaerobic system were examined. There were 6 types of substituent groups on nitrobenzene, including methyl, chlorine, amino, carboxyl, hydroxyl and cyanogen groups, at the ortho, meta or para positions. Our results showed that reduction followed pseudo-first order reaction kinetics, and that the rate constant (logkSA) varied widely, ranging between -4.77 and -2.82, depending upon the type and position of the substituent. A quantitative structure-activity relationship (QSAR) model using 15 theoretical molecular descriptors and partial-least-squares (PLS) regression was developed for the reduction rates of mono-NACs catalyzed by AC. The cross-validated regression coefficient (Qcum(2), 0.861) and correlation coefficient (R(2), 0.898) indicated significantly high robustness of the model. The VIP (variable importance in the projection) values of energy of the lowest unoccupied molecular orbital (ELUMO) and the maximum net atomic charge on the aromatic carbon bound to the nitro group (QC(-)) were 1.15 and 1.01, respectively. These values indicated that the molecular orbital energies and the atomic net charges might play important roles in the reduction of mono-NACs catalyzed by AC in anaerobic systems.

The use of carbon black to catalyze the reduction of nitrobenzenes by sulfides

Using carbon black (CB) as catalyst, the reduction of nitrobenzenes (NBs) to anilines by sulfides at room temperature was studied. In the reactions, CB serves as an intermedium to accelerate the reduction of NBs by sulfides. In the presence of 0.3g/L CB and 3.0 mM sulfides at pH 7.0 and 25°C, our results showed that CB-catalyzed reduction of NBs were pseudo-first order. The reduction rate constant of nitrobenzene was 0.0367 h(-1) in the presence of CB-1, which was 10 times more than the reduction rate constant in the absence of CB-1. Other experiments of different CB samples produced by different methods and different raw materials indicated that some active oxygenated functional groups on CB surface should be the reactive sites and play the dominant role in catalyzing the reduction of NBs. The catalytic reactions of different NBs by sulfides indicated that the reduction rate constants of chloronitrobenzenes to chloroanilines were greater than those of methylnitrobenzenes to methylanilines. And due to the effect of different substituent positions, the nitro group with meta substituent was reduced most easily while the nitro group with ortho substituent was reduced most difficulty.

QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action

The Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC₅₀) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. The Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. The Partial Least Squares (PLS) statistical approach was then used to develop 2D QSAR models. Validated PLS models were explored to: (1) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (2) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; and (3) analyse the role of various physical-chemical factors responsible for compounds' toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (r²(ext) = 0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modelling and 76% for external set).

An Integrated Decision-tree Testing Strategy for Skin Sensitisation with Respect to the Requirements of the EU REACH Legislation

This report presents some of the results of a joint research project, sponsored by Defra and conducted by FRAME and Liverpool John Moores University, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity end-points associated with the REACH system. This report focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for skin sensitisation testing. The manuscript reviews in vitro tests based on protein-ligand binding, dendritic/Langerhans cells and T-lymphocyte activation, and also the QSAR models and expert systems available for this endpoint. These tests are then incorporated into an integrated, decision-tree testing strategy, which also includes the Local Lymph Node Assay (in its original and new reduced protocols) and the traditional guinea-pig tests (which should only be used as a last resort). The aim of the strategy is to minimise the use of animals in testing for skin sensitisation, while satisfying the scientific and logistical demands of the EU REACH legislation.

Determinants of skin sensitisation potential

Skin sensitisation is an important toxicological endpoint. The possibility that chemicals used in the workplace and in consumer products might cause skin sensitisation is a major concern for individuals, for employers and for marketing. In European REACH (Registration, Evaluation, and Authorisation of Chemicals) legislation, the sensitising potential should therefore be assessed for chemicals below the 10 ton threshold. Development of methods for prediction of skin sensitisation potential without animal testing has been an active research area for some time, but has received further impetus with the advent of REACH and the EU Cosmetics Directive (EU 2003). This paper addresses the issue of non-animal based prediction of sensitisation by a mechanistic approach. It is known that the sequence of molecular, biomolecular and cellular events between exposure to a skin sensitiser and development of the sensitised state involves several stages, in particular penetration through the stratum corneum, covalent binding to carrier protein, migration of Langerhans cells, presentation of the antigen to naïve T-cells. In this paper each of these stages is considered with respect to the extent to which it is dependent on the chemical properties of the sensitiser. The evidence suggests that, although penetration of the stratum corneum, stimulation of migration and maturation of Langerhans cells, and antigen recognition are important events in the induction of sensitisation, except in certain specific circumstances they can be taken for granted. They are not important factors in determining whether a compound will be a sensitiser or not, nor are they important factors in determining how potent one sensitiser will be relative to another. The ability to bind covalently to carrier protein is the major structure-dependent determinant of skin sensitisation potential. A chemistry-based prediction strategy is proposed involving reaction mechanistic domain assignment, reactivity and hydrophobicity determination, and application of quantitative mechanistic modelling (QMM) or read-across.

Structure-toxicity relationships of nitroaromatic compounds

The toxicity data of 28 nitroaromatic compounds (nitrobenzenes and, for comparison, benzene and toluene) related to a 50% lethal dose concentration for rats (LD50) were used to develop quantitative structure-activity relationships (QSARs).A genetic algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. The obtained equations consist of one to three descriptors. A number of molecular descriptors was obtained from HF/6-31G(d) and DFT (B3LYP/6-311+G(d, p)) level calculations. The calculated molecular geometry and electronic properties were evaluated by comparison with the available experimental data (where applicable). All parameters obtained at the B3LYP/6-311+G(d, p) level and the topological descriptors derived from this geometry were found to be reliable, except for dipole moment, due to the large uncertainty of its estimation. Satisfactory relationships were observed for the one-parameter structure-toxicity models between topological (X5Av, Ms) and quantum-chemical (ELUMO) descriptors. For better predictability two- and three-parameter QSAR analyses were performed. These analyses resulted in much better equations with correlation coefficient values r = 0.872-0.924. These models have been obtained with a set of topological, fragment and quantum-chemical descriptors (Ms, PCR, PCD, BELe1, C-026 and ELUMO). The toxicity of nitroaromatic compounds appears to be governed by a number of factors, such as the number of nitrogroups, the electrotopological state, the presence of certain fragments and the electrophilicity/reactivity parameter (ELUMO). Nitrobenzenes exhibited electrophilic reactivity (as was shown by correlation of the toxicity with the energy of the lowest unoccupied orbital, ELUMO). The toxicity LD50 parameter for rats has been utilized for the first time for QSAR analysis of nitrobenzenes. The predictive ability of the models is determined by a cross-validation "leave-one-out" method.

Predicting multiple drugs side effects with a general drug-target interaction thermodynamic Markov model

Most of present molecular descriptors just consider the molecular structure. In the present article we pretend extending the use of Markov chain models to define novel molecular descriptors, which consider in addition to molecular structure other parameters like target site or toxic effect. Specifically, this molecular descriptor takes into consideration not only the molecular structure but the specific system the drug affects too. Herein, it is developed a general Markov model that describes 39 different drugs side effects grouped in 11 affected systems for 301 drugs, being 686 cases finally. The data was processed by linear discriminant analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/100% for systemic phenomena (47 out of 47)/(12 out of 12) and metabolic (18 out of 18)/(5 out of 5), muscular-skeletal (23 out of 23)/(6 out of 6) and neurological manifestations (33 out of 33)/(8 out of 8); 97.6/96.7% for cardiovascular manifestation (122 out of 125)/(30 out of 31); 97.1/97.5% for breathing manifestations (34 out of 35)/(8 out of 9); 97/99.4% for gastrointestinal manifestations (159 out of 164)/(40 out of 41); 97/95% for endocrine manifestations (32 out of 33)/(7 out of 8); 96.4/94.6% for psychiatric manifestations (53 out of 55)/(13 out of 14); 95.1/99.1% for hematological manifestations (98 out of 103)/(25 out of 26) and 88/92.3% for dermal manifestations (44 out of 50)/(12 out of 13). In addition, we report preliminary experimental reversible decrease of lymphocytes differential count after administration of the antibacterial drug G-1 in mice, which coincide with a posterior probability (P%=74.91) predicted by the model. This article develops a model that encompasses a large number of side effects grouped in specific organ systems in a single stochastic framework for the first time.

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

Inhibition of growth of the yeast Saccharomyces cerevisiae (Cmiz, the minimum concentration that produced a clear inhibition zone within 12 h) for 24 nitroaromatic compounds was investigated and a quantitative structure-activity relationship (QSAR) developed based on hydrophobicity expressed as the l-octanol/water partition coefficient in logarithm form, log K(ow), electrophilicity based on the energy of the lowest unoccupied orbital (E(lumo)). All nitrobenzene derivatives exhibited enhanced reactive toxicity than baseline. The toxicities of mono-nitrobenzenes and di-nitrobenzenes were elicited by different mechanisms of toxic action. For mono-nitro-derivatives, both significant log K(ow) based and strong E(lumo)-dependent relationships were observed indicating that their toxicities were affected both by the penetration process and the interaction with target sites of interaction. The toxicities of di-nitrobenzenes were greater than mono-nitrobenzenes and no log K(ow)-dependent but highly significant E(lumo)-based relationship was obtained. This suggests that toxicity of di-nitrobenzenes was highly electrophilic and involved mainly their in vivo electrophilic interaction with biomacromolecules. In an effort to model the elevated toxicity of all nitrobenzenes, a response-surface analysis was performed and this resulted in a highly predictive two-variable QSAR without reference to their exact mechanisms (Cmiz = 0.41 log K(ow) - 0.89 E(lumo) - 0.46, r2 = 0.87, Q2 = 0.86, n = 24).

Quantitative structure-toxicity relationships using TOPS-MODE. 2. Neurotoxicity of a non-congeneric series of solvents

Neurotoxicities of a series of solvents in rats and mice have been modeled by means of the TOPS-MODE approach. Two quantitative structure-toxicity relationship (QSTR) models were obtained explaining more than 80% of the variance in the experimental values of neurotoxicity of 45 solvents. Only one compound was detected as statistical outlier for these models. In contrast, previous models explained less than 60% of the variance in this property for 44 solvents. Finally, the contributions to neurotoxicity in rats and mice for a series of structural fragments were found. Structural characteristics of chlorinated fragments responsible for their different neurotoxicities were analyzed. The differences in neurotoxic behavior of some fragments in rats and mice were also analyzed, which could give insights on the toxicological mechanism of action of solvents studied.

Quantitative structure--toxicity relationships using TOPS-MODE. 1. Nitrobenzene toxicity to Tetrahymena pyriformis

The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach (Estrada, E. SAR QSAR Environ. Res. 2000, 11, 55-73) has been introduced to the study of toxicological properties. The toxicity of 42 nitrobenzenes was studied with this approach obtaining a good quantitative structure--toxicity model. For the first time we compare the use of eight different weights in the diagonal entries of the bond matrix for selecting the best TOPS-MODE model. TOPS-MODE was used to derive the contribution of different fragments to the toxicity of studied compounds. These contributions were applied to calculate toxicity substituent constants for the groups present in the nitrobenzenes studied.