QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds

Dental Resin-Based Luting Materials-Review

As cementation represents the last stage of the work involved in making various indirect restorations (metal ceramic crowns and bridges, full ceramic crowns and bridges, inlays, onlays, and fiber posts), its quality significantly contributes to the clinical success of the therapy performed. In the last two decades, the demand for ceramic indirect restorations in everyday dental practice has considerably increased primarily due to the growing significance of esthetics among patients, but also as a result of hypersensitivity reactions to dental alloys in some individuals. In this context, it is essential to ensure a permanent and reliable adhesive bond between the indirect restoration and the tooth structure, as this is the key to the success of aesthetic restorations. Resin-based luting materials benefit from excellent optical (aesthetic) and mechanical properties, as well as from providing a strong and durable adhesive bond between the restoration and the tooth. For this reason, resin cements are a reliable choice of material for cementing polycrystalline ceramic restorations. The current dental material market offers a wide range of resin cement with diverse and continually advancing properties. In response, we wish to note that the interest in the properties of resin-based cements among clinicians has existed for many years. Yet, despite extensive research on the subject and the resulting continued improvements in the quality of these materials, there is still no ideal resin-based cement on the market. The manuscript authors were guided by this fact when writing the article content, as the aim was to provide a concise overview of the composition, properties, and current trends, as well as some future guidelines for research in this field that would be beneficial for dental practitioners as well as the scientific community. It is extremely important to provide reliable and succinct information and guidelines for resin luting materials for dental dental practitioners.

The enhancement scheme for the predictive ability of QSAR: A case of mutagenicity

Mutagenicity is one of the most dangerous properties from the point of view of medicine and ecology. Experimental determination of mutagenicity remains a costly process, which makes it attractive to identify new hazardous compounds based on available experimental data through in silico methods or quantitative structure-activity relationships (QSAR). A system for constructing groups of random models is proposed for comparing various molecular features extracted from SMILES and graphs. For mutagenicity (mutagenicity values were expressed by the logarithm of the number of revertants per nanomole assayed by Salmonella typhimurium TA98-S9 microsomal preparation) models, the Morgan connectivity values are more informative than the comparison of quality for different rings in molecules. The resulting models were tested with the previously proposed model self-consistency system. The average value of the determination coefficient for the validation set is 0.8737 ± 0.0312.

Can Essential Oils Be a Natural Alternative for the Control of Spodoptera frugiperda? A Review of Toxicity Methods and Their Modes of Action

Spodoptera frugiperda is a major pest of maize crops. The application of synthetic insecticides and the use of Bt maize varieties are the principal strategies used for its control. However, due to the development of pesticide resistance and the negative impact of insecticides on the environment, natural alternatives are constantly being searched for. Accordingly, the objective of this review was to evaluate the use of essential oils (EOs) as natural alternatives for controlling S. frugiperda. This review article covers the composition of EOs, methods used for the evaluation of EO toxicity, EO effects, and their mode of action. Although the EOs of Ocimum basilicum, Piper marginatum, and Lippia alba are the most frequently used, Ageratum conyzoides, P. septuplinervium. O. gratissimum and Siparuna guianensis were shown to be the most effective. As the principal components of these EOs vary, then their mode of action on the pest could be different. The results of our analysis allowed us to evaluate and compare the potential of certain EOs for the control of this insect. In order to obtain comparable results when evaluating the toxicity of EOs on S. frugiperda, it is important that methodological issues are taken into account.

Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction

A synergistic approach that combines machine learning with semi-empirical methods enables the fast and accurate prediction of DFT-quality reaction barriers, with mechanistic insights available from semi-empirical transition state geometries.

First multi-target QSAR model for predicting the cytotoxicity of acrylic acid-based dental monomers

OBJECTIVE

Acrylic acid derivatives are frequently used as dental monomers and their cytotoxicity towards various cell lines is well documented. This study aims to probe the structural and physicochemical attributes responsible for higher toxicity of dental monomers, using quantitative structure-activity relationships (QSAR) modeling approaches.

METHODS

A regression-based linear single-target QSAR (st-QSAR) model was developed with a comparatively small dataset containing 39 compounds, the cytotoxicity of which has been assessed over the Hela S3 cell line. By contrast, a classification-based multi-target QSAR model was developed with 138 compounds, the cytotoxicity of which has been reported against 18 different cell lines. Both models were set up following rigorous validation protocols confirming their statistical significance and robustness.

RESULTS

The performance of the linear mt-QSAR model, developed with various feature selection and post-selection similarity searching-based schemes, superseded that of all non-linear models produced with six machine learning methods by hyperparameter optimization. The final derived st-QSAR and mt-QSAR linear models are shown to be highly predictive, as well as revealing the crucial structural and physicochemical factors responsible for higher cytotoxicity of the dental monomers.

SIGNIFICANCE

This study is the first attempt on unveiling the cytotoxicity of dental monomers over several cell lines by means of a single multi-target QSAR model. Further, such a model is ready to get widespread applicability in the screening of new monomers, judging from its almost accurate predictions over diverse experimental assay conditions.

Reactivity prediction in aza-Michael additions without transition state calculations: the Ames test for mutagenicity

Animal testing remains a contentious ethical issue in predictive toxicology. Thus, a fast, versatile, low-cost quantum chemical model is presented for predicting the risk of Ames mutagenicity in a series of 1,4 Michael acceptor type compounds. This framework eliminates the need for transition state calculations, and uses an intermediate structure to probe the reactivity of aza-Michael acceptors. This model can be used in a variety of settings e.g., the design of targeted covalent inhibitors and polyketide biosyntheses.

Cytotoxicity and Pro-/Anti-inflammatory Properties of Cinnamates, Acrylates and Methacrylates Against RAW264.7 Cells

BACKGROUND/AIM

Periodontitis is a chronic inflammatory disease linked to various systemic age-related conditions. It is known that α,β-unsaturated carbonyl compounds such as dietary cinnamates (β-phenyl acrylates) and related (meth)acrylates can have various positive and negative health effects, including cytotoxicity, allergic activity, pro-and anti-inflammatory activity, and anticancer activity. To clarify the anti-inflammatory properties of α,β-unsaturated carbonyl compounds without a phenolic group in the context of periodontal tissue inflammation and alveolar bone loss, we investigated the cytotoxicity and up-regulatory/down-regulatory effect of three trans-cinnamates (trans-cinnamic acid, methyl cinnamate, trans-cinnamaldehyde), two acrylates (ethyl acrylate, 2-hydroxyethyl acrylate), and three methacrylates (methyl methacrylate, 2-hydroxyethyl methacrylate, and triethyleneglycol dimethacrylate) using RAW264.7 cells.

MATERIALS AND METHODS

Cytotoxicity was determined using a cell counting kit (CCK-8) and mRNA expression was determined using real-time reverse transcriptase-polymerase chain reaction (RT-PCR). Pro-inflammatory and anti-inflammatory properties were assessed in terms of expression of mRNAs for cyclo-oxygenase-2 (Cox2), nitric oxide synthase 2 (Nos2), tumor necrosis factor-alpha (Tnfa) and heme oxygenase 1 (Ho1).

RESULTS

The most cytotoxic compound was 2-hydroxyethyl acrylate, followed by ethyl acrylate and cinnamaldehyde (50% lethal cytotoxic concentration, LC₅₀=0.2-0.5 mM). Cox2 mRNA expression was up-regulated by cinnamaldehyde and 2-hydroxyethyl acrylate, particularly by the former. In contrast, the up-regulatory effect on Nos2 mRNA expression was in the order: cinnamaldehyde >> ethyl acrylate ≈ triethyleneglycol dimethacrylate >> methyl methacrylate ≈ methyl cinnamate. On the other hand, cinnamic acid and 2-hydroxyethyl methacrylate had no effect on gene expression. The two acrylates, but not cinnamates and methacrylates, up-regulated the expression of Ho1 mRNA at a non-cytotoxic concentration of 0.1 mM. Expression of Cox2, Nos2 and Tnfa mRNAs induced by Porphyromonas gingivalis lipopolysaccharide was greatly suppressed by cinnamaldehyde, methyl cinnamate and the two acrylates at 0.1 mM (p<0.05), and slightly, but significantly suppressed by cinnamic acid and methacrylates at 0.1-1 mM (p<0.05).

CONCLUSION

Cinnamaldehyde and acrylates exhibited both anti-inflammatory and pro-inflammatory properties, possibly due to their marked ability to act as Michael reaction acceptors, as estimated from the beta-carbon ¹³C-nuclear magnetic resonance spectra. Methyl cinnamate exhibited potent anti-inflammatory activity with less cytotoxicity and pro-inflammatory activity, suggesting that this compound may be useful for treatment of periodontal disease and related systemic diseases.

Advances in distributed computing with modern drug discovery

INTRODUCTION

Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.

Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential

Mutagenicity is the ability of a substance to induce mutations. This hazardous ability of a substance is decisive from point of view of ecotoxicology. The number of substances, which are used for practical needs, grows every year. Consequently, methods for at least preliminary estimation of mutagenic potential of new substances are necessary. Semi-correlations are a special case of traditional correlations. These correlations can be named as "correlations along two parallel lines." This kind of correlation has been tested as a tool to predict selected endpoints, which are represented by only two values: "inactive/active" (0/1). Here this approach is used to build up predictive models for mutagenicity of large dataset (n = 3979). The so-called index of ideality of correlation (IIC) has been tested as a statistical criterion to estimate the semi-correlation. Three random splits of experimental data into the training, invisible-training, calibration, and validation sets were analyzed. Two models were built up for each split: the first model based on optimization without the IIC and the second model based on optimization where IIC is involved in the Monte Carlo optimization. The statistical characteristics of the best model (calculated with taking into account the IIC) n = 969; sensitivity = 0.8050; specificity = 0.9069; accuracy = 0.8648; Matthews's correlation coefficient = 0.7196 (using IIC). Thus, the use of IIC improves the statistical quality of the binary classification models of mutagenic potentials (Ames test) of organic compounds.

Electron-correlation based externally predictive QSARs for mutagenicity of nitrated-PAHs in Salmonella typhimurium TA100

In quantitative modeling, there are two major aspects that decide reliability and real external predictivity of a structure-activity relationship (SAR) based on quantum chemical descriptors. First, the information encoded in employed molecular descriptors, computed through a quantum-mechanical method, should be precisely estimated. The accuracy of the quantum-mechanical method, however, is dependent upon the amount of electron-correlation it incorporates. Second, the real external predictivity of a developed quantitative SAR (QSAR) should be validated employing an external prediction set. In this work, to analyze the role of electron-correlation, QSAR models are developed for a set of 51 ubiquitous pollutants, namely, nitrated monocyclic and polycyclic aromatic hydrocarbons (nitrated-AHs and PAHs) having mutagenic activity in TA100 strain of Salmonella typhimurium. The quality of the models, through state-of-the-art external validation procedures employing an external prediction set, is compared to the best models known in the literature for mutagenicity. The molecular descriptors whose electron-correlation contribution is analyzed include total energy, energy of HOMO and LUMO, and commonly employed electron-density based descriptors such as chemical hardness, chemical softness, absolute electronegativity and electrophilicity index. The electron-correlation based QSARs are also compared with those developed using quantum-mechanical descriptors computed with advanced semi-empirical (SE) methods such as PM6, PM7, RM1, and ab initio methods, namely, the Hartree-Fock (HF) and the density functional theory (DFT). The models, developed using electron-correlation contribution of the quantum-mechanical descriptors, are found to be not only reliable but also satisfactorily predictive when compared to the existing robust models. The robustness of the models based on descriptors computed through advanced SE methods, is also observed to be comparable to those developed with the electron-correlation based descriptors. The work emphasizes that the correlation-energy can serve as a reliable descriptor to explore the origin of biological activities at the level of electron-dynamics.

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.

Classification models for safe drug molecules

Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.

Two new parameters based on distances in a receiver operating characteristic chart for the selection of classification models

There are several indices that provide an indication of different types on the performance of QSAR classification models, being the area under a Receiver Operating Characteristic (ROC) curve still the most powerful test to overall assess such performance. All ROC related parameters can be calculated for both the training and test sets, but, nevertheless, neither of them constitutes an absolute indicator of the classification performance by themselves. Moreover, one of the biggest drawbacks is the computing time needed to obtain the area under the ROC curve, which naturally slows down any calculation algorithm. The present study proposes two new parameters based on distances in a ROC curve for the selection of classification models with an appropriate balance in both training and test sets, namely the following: the ROC graph Euclidean distance (ROCED) and the ROC graph Euclidean distance corrected with Fitness Function (FIT(λ)) (ROCFIT). The behavior of these indices was observed through the study on the mutagenicity for four genotoxicity end points of a number of nonaromatic halogenated derivatives. It was found that the ROCED parameter gets a better balance between sensitivity and specificity for both the training and prediction sets than other indices such as the Matthews correlation coefficient, the Wilk's lambda, or parameters like the area under the ROC curve. However, when the ROCED parameter was used, the follow-on linear discriminant models showed the lower statistical significance. But the other parameter, ROCFIT, maintains the ROCED capabilities while improving the significance of the models due to the inclusion of FIT(λ).

Alert-QSAR. Implications for electrophilic theory of chemical carcinogenesis

Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations A(SA) of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD(50)], i.e., [Formula: see text]). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., [Formula: see text]. We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles.

Consensus ranking approach to understanding the underlying mechanism with QSAR

Constructing a highly predictive model and exploiting the underlying mechanism associated with a specific property of chemicals are the two main goals of quantitative structure-activity relationship analysis (QSAR). However, the latter has long been carried out as a byproduct of model construction. Here we confirmed for the first time in this study that conventional descriptor selection methods designed to develop a best predictive model are likely not suitable for mechanistic analysis, i.e., the selected descriptors strongly depended on the selection of chemicals in the training sets. As an alternative, a consensus ranking protocol was proposed to select a robust descriptor set for mechanistic analysis, which can successfully overcome the above shortcoming. Moreover, the consistently inferior model performance using descriptors selected for mechanistic analysis suggested the irreplaceable role of model development in achieving models with the best predictive capability.