Influence of structural and functional modifications of selected genotoxic carcinogens on metabolism and mutagenicity – a review

Alterations in molecular structure are responsible for the differential biological response(s) of a chemical inside a biosystem. Structural and functional parameters that govern a chemical's metabolic course and determine its ultimate outcome in terms of mutagenic/carcinogenic potential are extensively reviewed here. A large number of environmentally-significant organic chemicals are addressed under one or more broadly classified groups each representing one or more characteristic structural feature. Numerous examples are cited to illustrate the influence of key structural and functional parameters on the metabolism and DNA adduction properties of different chemicals. It is hoped that, in the event of limited experimental data on a chemical's bioactivity, such knowledge of the likely roles played by key molecular features should provide preliminary information regarding its bioactivation, detoxification and/or mutagenic potential and aid the process of screening and prioritising chemicals for further testing.     

Searching for an enhanced predictive tool for mutagenicity

The Multiple Computer Automated Structure Evaluation (MCASE) program was used to evaluate the mutagenic potential of organic compounds. The experimental Ames test mutagenic activities for 2513 chemicals were collected from various literature sources. All chemicals have experimental results in one or more Salmonella tester strains. A general mutagenicity data set and fifteen individual Salmonella test strain data sets were compiled. Analysis of the learning sets by the MCASE program resulted in the derivation of good correlations between chemical structure and mutagenic activity. Significant improvement was obtained as more data was added to the learning databases when compared with the results of our previous reports. Several biophores were identified as being responsible for the mutagenic activity of the majority of active chemicals in each individual mutagenicity module. It was shown that the multiple-database mutagenicity model showed a clear advantage over normally used single-database models. The expertise produced by this analysis can be used to predict the mutagenic potential of new compounds.     

Molecular dynamics, density functional, ADMET predictions, virtual screening, and molecular interaction field studies for identification and evaluation of novel potential CDK2 inhibitors in cancer therapy

In this work, we have used molecular dynamics, density functional theory, virtual screening, ADMET predictions, and molecular interaction field studies to design and propose eight novel potential inhibitors of CDK2. The eight molecules proposed showed interesting structural characteristics that are required for inhibiting the CDK2 activity and show potential as drug candidates for the treatment of cancer. The parameters related to the Rule of Five were calculated, and only one of the molecules violated more than one parameter. One of the proposals and one of the drug-like compounds selected by virtual screening indicated to be promising candidates for CDK2-based cancer therapy.     

O?+?C2H4 potential energy surface: lowest-lying singlet at the multireference level

Extensive density functional theory (DFT) calculations have been performed to develop a force field for the classical molecular dynamics (MD) simulations of various azobenzene derivatives. Besides azobenzene, we focused on a thiolated azobenzene’s molecular rod (4′-{[(1,1′-biphenyl)-4-yl]diazenyl}-(1,1′-biphenyl)-4-thiol) that has been previously demonstrated to photoisomerize from trans to cis with high yields on surfaces. The developed force field is an extension of OPLS All Atoms, and key bonding parameters are parameterized to reproduce the potential energy profiles calculated by DFT. For each of the parameterized molecule, we propose three sets of parameters: one best suited for the trans configuration, one for the cis configuration, and finally, a set able to describe both at a satisfactory degree. The quality of the derived parameters is evaluated by comparing with structural and vibrational experimental data. The developed force field opens the way to the classical MD simulations of self-assembled monolayers (SAMs) of azobenzene’s molecular rods, as well as to the quantum mechanics/molecular mechanics study of photoisomerization in SAMs.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global ( E HOMO ) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global (E(HOMO)) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

In-silico screening of high production volume chemicals for mutagenicity using the MCASE QSAR expert system

Computational screening is suggested as a way to set priorities for further testing of high production volume (HPV) chemicals for mutagenicity and other toxic endpoints. Results are presented for batch screening of 2484 HPV chemicals to predict their mutagenicity in Salmonella typhimurium (Ames test). The chemicals were tested against 15 databases for Salmonella strains TA100, TA1535, TA1537, TA97 and TA98, both with metabolic activation (using rat liver and hamster liver S9 mix test) and without metabolic activation. Of the 2484 chemicals, 1868 are predicted to be completely nonmutagenic in all of the 15 data modules and 39 chemicals were found to contain structural fragments outside the knowledge of the expert system and therefore suggested for further evaluation. The remaining 616 chemicals were found to contain different biophores (structural alerts) believed to be linked to mutagenicity. The chemicals were ranked indescending order according to their predicted mutagenic potential and the first 100 chemicals with highest mutagenicity scores are presented. The screening result offers hope that rapid and inexpensive computational methods can aid in prioritizing the testing of HPV chemicals, save time and animals and help to avoid needless expense.     

In-Silico Screening of High Production Volume Chemicals for Mutagenicity using the mcase QSAR Expert System

Computational screening is suggested as a way to set priorities for further testing of high production volume (HPV) chemicals for mutagenicity and other toxic endpoints. Results are presented for batch screening of 2484 HPV chemicals to predict their mutagenicity in Salmonella typhimurium (Ames test). The chemicals were tested against 15 databases for Salmonella strains TA100, TA1535, TA1537, TA97 and TA98, both with metabolic activation (using rat liver and hamster liver S9 mix test) and without metabolic activation. Of the 2484 chemicals, 1868 are predicted to be completely nonmutagenic in all of the 15 data modules and 39 chemicals were found to contain structural fragments outside the knowledge of the expert system and therefore suggested for further evaluation. The remaining 616 chemicals were found to contain different biophores (structural alerts) believed to be linked to mutagenicity. The chemicals were ranked in descending order according to their predicted mutagenic potential and the first 100 chemicals with highest mutagenicity scores are presented. The screening result offers hope that rapid and inexpensive computational methods can aid in prioritizing the testing of HPV chemicals, save time and animals and help to avoid needless expense.     

Topological structural alerts modulations of mammalian cell mutagenicity for halogenated derivatives

Genotoxicity is a key toxicity endpoint for current regulatory requirements regarding new and existing chemicals. However, genotoxicity testing is time-consuming and costly, and involves the use of laboratory animals. This has motivated the development of computational approaches, designed to predict genotoxicity without the need to conduct laboratory tests. Currently, many existing computational methods, like quantitative structure–activity relationship (QSAR) models, provide limited information about the possible mechanisms involved in mutagenicity or predictions based on structural alerts (SAs) do not take statistical models into account. This paper describes an attempt to address this problem by using the TOPological Substructural MOlecular Design (TOPS-MODE) approach to develop and validate improved QSAR models for predicting the mutagenicity of a range of halogenated derivatives. Our most predictive model has an accuracy of 94.12%, exhibits excellent cross-validation and external set statistics. A reasonable interpretation of the model in term of SAs was achieved by means of bond contributions to activity. The results obtained led to the following conclusions: primary halogenated derivatives are more mutagenic than secondary ones; and substitution of chlorine by bromine increases mutagenicity while polyhalogenation decreases activity. The paper demonstrates the potential of the TOPS-MODE approach in developing QSAR models for identifying structural alerts for mutagenicity, combining high predictivity with relevant mechanistic interpretation.     

Molecular and quantitative aspects of the chemistry of fulvic acid and its interactions with metal ions and organic chemicals : Part II. The fluorescence spectroscopy approach

Information in the literature on the use of fluorescence spectroscopy for the study of the chemistry of fulvic acid and its interactions with metal ions and organic chemicals is discussed. Basic principles, instrumentation, procedures, methodology and limitations of the fluorescence technique and the fluorescence polarization method are briefly summarized. This is followed by an extended discussion on the direct information that fluorescence spectra can furnish on the properties and nature of fluorescing structures in fulvic acids. The effects of molecular parameters and environmental factors (molecular weight, concentration, pH, ionic strength, temperature and redox potential) on the fluorescence behaviour of fulvic acids are discussed. Particular attention is devoted to the indirect information that fluorescence quenching and fluorescence polarization studies provide on molecular and quantitative aspects of the chemistry of fulvic acid in solution, especially in relation to molecular conformation and binding with metal ions and organic chemicals. The potential advantage of this non-separative, non-destructive technique for the study of environmental samples such as fulvic acids is emphasized.     

Highlevel ab initio and hybrid density functional theory study of the energy profile for the CO+CO reaction

Two complete basis set and three hybrid density functional computational studies were applied in the exploration of the ¹CO+²CO⁺ reaction potential energy surface. One molecular carbon monoxide–carbon monoxide cation molecular associate was elucidated as the structure with the lowest energy on the potential energy surface. Ionization energies, bond dissociation energies, and enthalpies of formation for every di and tri-atomic molecule on the potential energy surface were estimated with the two complete basis sets and the three hybrid density functional theory methods. Six different endothermic channels for the ¹CO+²CO⁺ reaction were evaluated with ab initio and DFT methods. The computed energies and structural parameters are compared with experimental values where available. Some new energies for this reaction system were suggested.     

Impact of graphene-based nanomaterials (GBNMs) on the structural and functional conformations of hepcidin peptide

Graphene-based nanomaterials (GBNMs) are widely used in various industrial and biomedical applications. GBNMs of different compositions, size and shapes are being introduced without thorough toxicity evaluation due to the unavailability of regulatory guidelines. Computational toxicity prediction methods are used by regulatory bodies to quickly assess health hazards caused by newer materials. Due to increasing demand of GBNMs in various size and functional groups in industrial and consumer based applications, rapid and reliable computational toxicity assessment methods are urgently needed. In the present work, we investigate the impact of graphene and graphene oxide nanomaterials on the structural conformations of small hepcidin peptide and compare the materials for their structural and conformational changes. Our molecular dynamics simulation studies revealed conformational changes in hepcidin due to its interaction with GBMNs, which results in a loss of its functional properties. Our results indicate that hepcidin peptide undergo severe structural deformations when superimposed on the graphene sheet in comparison to graphene oxide sheet. These observations suggest that graphene is more toxic than a graphene oxide nanosheet of similar area. Overall, this study indicates that computational methods based on structural deformation, using molecular dynamics (MD) simulations, can be used for the early evaluation of toxicity potential of novel nanomaterials.     

QSAR Study of Nitrobenzenes’ Toxicity to Tetrahymena Pyriformis Using Semi-empirical Quantum Chemical Methods

1 INTRODUCTION Nitrobenzenes are an important class of industrial chemicals produced with substantial marketing volu-mes and a diverse range of use patterns[1].They are widely used as pesticides,solvents,explosives,dye-stuffs,etc.Most of the nitrobenzenes together with their derivatives are toxic,and they can cause dam-nification of human kidney,liver,eye,skin,blood system and so on[2～4].It is reported that all nitro-benzenes and their metabolites have mutagenicity,which induces the cyto…     

纤维素及其衍生物催化脱氧转化制化学品

Biomass, as a renewable carbon resource in nature, has been considered as an ideal starting feedstock to produce various valuable chemicals, fuels, and materials, and thus, can help build a sustainable chemical industry. Because cellulose is one of the richest components in lignocellulosic biomass, the efficient transformation of cellulose plays a crucial role in biomass utilization. However, there are many oxygen-containing groups in cellulose, and therefore, the selective removal of particular functional groups from cellulose becomes an essential step in the synthesis of the chemicals or fuels that can meet the requirements set by current chemical industries. In the past decades, several efficient catalytic systems have been developed to selectively split the C―O bonds inside cellulose and its derivatives, thereby producing various valuable chemicals. In this review article, we highlight recent progress made in the selective deoxygenation of cellulose and its derived key platforms such as glucose and 5-hydroxymethyl furfural (HMF) into ethanol, dimethyl furfural (DMF), 1, 6-hexanediol (1, 6-HD), and adipic acid. The selection of these reactions is primarily because these chemicals are of great significance in chemical industries. More importantly, the formation of these chemicals represents the cleavage of different C―O bonds in biomass molecules. For instance, the synthesis of ethanol requires cleaving of only one C―O bond and two C―C bonds of the glucose unit inside cellulose. If two or more C―O bonds in the sugar or sugar acids are cleaved, olefins, oxygen-reduced sugars, and adipic acid will be attained. HMF has a furan ring linked by hydroxyl/carbonyl groups, and hence, either a furanic compound (e.g., DMF) or linear products (e.g., 1, 6-HD and adipic acid) can be synthesized by selective removal of hydroxyl/carbonyl oxygen or ring oxygen atoms. This article focuses on the selective cleavage of particular C―O bonds via heterogeneous catalysis. Efficient catalytic systems using hydrogenolysis and/or deoxydehydration strategies for these transformations are discussed. Moreover, the functions of typical catalysts and reaction mechanisms are presented to obtain insight into the C―O bond cleavage in these biomass molecules. Additionally, other factors such as reaction conditions that also influence the deoxygenation performance are analyzed. We hope that these knowledge gained on the catalytic deoxygenation of cellulose and its derived platforms will promote the rational design of effective strategies or catalysts in the future utilization of lignocellulosic biomass.     

纤维素及其衍生物催化脱氧转化制化学品

纤维素是木质纤维素生物质中最为丰富的组分,将其催化转化制备高附加值化学品在生物质资源化利用中占据极为重要的一席之地。由于纤维素中氧含量过高,需选择性地脱除部分氧原子才可获得满足当前化学工业对各类高值化学品的要求。近年来,针对纤维素以及由其衍生的关键平台分子葡萄糖和5-羟甲基糠醛(HMF)等催化脱氧的研究已引起广泛关注,并取得诸多重要进展。在此,我们总结了具有代表性的多相催化剂体系,讨论了利用氢解或脱水脱氧策略分别将纤维素和葡萄糖等分子中一个或多个C―O键裁剪制备乙醇、烯烃或己二酸等的研究。我们还着重介绍了HMF和其衍生的呋喃化合物选择性剪切C―OH/C＝O键或呋喃环中的C―O―C键分别制备二甲基呋喃和1, 6-己二醇等催化体系。此外,对各多相催化剂的作用机制和特定C―O断键机理也分别进行了探讨,以期深入理解纤维素及其衍生物的催化脱氧反应。     

Computer-assisted studies of molecular structure-biological activity relationships

Computer-assisted methods can be used to investigate the relationships between the molecular structures of compounds and their biological activity. A number of approaches have been reported in the literature, including correlations of activity with substituent constants, conformational analysis and display, quantum mechanical methods, and methods relying on discriminant development and pattern-recognition techniques. Application areas for this technology include drug design, agricultural chemical design, and studies of chemical toxicity and genetic toxicity (mutagenic or carcinogenic potential). These structure-activity methods are introduced, and citations are given. Several current structure-activity relationship (SAR) studies using pattern recognition are presented as examples of typical projects that are feasible with this approach. These include the investigation of a set of 122 antiinflammatory steroids, a study of 153 retinoids for cancer prevention, and a study of chemicals that have been tested in a sister chromatid exchange mutagen screen.