A topological substructural molecular design approach for predicting mutagenesis end-points of alpha, beta-unsaturated carbonyl compounds

2D finger-printing and molecular docking studies identified potent mosquito repellents targeting odorant binding protein 1

Personal protection measures against the mosquitoes like the use of repellents constitute valuable tools in the effort to prevent the transmission of vector-borne diseases. Therefore, the discovery of novel repellent molecules which will be effective at lower concentrations and provide a longer duration of protection remains an urgent need. Mosquito Odorant-Binding Proteins (OBPs) involved in the initial steps of the olfactory signal transduction cascade have been recognized not only as passive carriers of odors and pheromones but also as the first molecular filter to discriminate semiochemicals, hence serving as molecular targets for the design of novel pest control agents. Among the three-dimensional structures of mosquito OBPs solved in the last decades, the OBP1 complexes with known repellents have been widely used as reference structures in docking analysis and molecular dynamics simulation studies for the structure-based discovery of new molecules with repellent activity. Herein, ten compounds known to be active against mosquitoes and/or displaying a binding affinity for Anopheles gambiae AgamOBP1 were used as queries in an in silico screening of over 96 million chemical samples in order to detect molecules with structural similarity. Further filtering of the acquired hits on the basis of toxicity, vapor pressure, and commercial availability resulted in 120 unique molecules that were subjected to molecular docking studies against OBP1. For seventeen potential OBP1-binders, the free energy of binding (FEB) and mode of interaction with the protein were further estimated by molecular docking simulations leading to the selection of eight molecules exhibiting the highest similarity with their parental compounds and favorable energy values. The in vitro determination of their binding affinity to AgamOBP1 and the evaluation of their repellent activity against female Aedes albopictus mosquitoes revealed that our combined ligand similarity screening and OBP1 structure-based molecular docking successfully detected three molecules with enhanced repellent properties. A novel DEET-like repellent with lower volatility (8.55 × 10^-4 mmHg) but a higher binding affinity for OBP1 than DEET (1.35 × 10^-3 mmHg). A highly active repellent molecule that is predicted to bind to the secondary Icaridin (sIC)-binding site of OBP1 with higher affinity than to the DEET-site and, therefore, represents a new scaffold to be exploited for the discovery of binders targeting multiple OBP sites. Finally, a third potent repellent exhibiting a high degree of volatility was found to be a strong DEET-site binder of OBP1 that could be used in slow-release formulations.

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database

Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.

A Classification Model to Identify Direct-Acting Mutagenic Polycyclic Aromatic Hydrocarbon Transformation Products

Polycyclic aromatic hydrocarbons (PAHs) are a complex group of environmental contaminants, many having long environmental half-lives. As these compounds degrade, the changes in their structure can result in a substantial increase in mutagenicity compared to the parent compound. Over time, each individual PAH can potentially degrade into several thousand unique transformation products, creating a complex, constantly evolving set of intermediates. Microbial degradation is the primary mechanism of their transformation and ultimate removal from the environment, and this process can result in mutagenic activation similar to the metabolic activation that can occur in multicellular organisms. The diversity of the potential intermediate structures in PAH-contaminated environments renders hazard assessment difficult for both remediation professionals and regulators. A mixture of structural and energetic descriptors has proven effective in existing studies for classifying which PAH transformation products will be mutagenic. However, most existing studies of environmental PAH mutagens primarily focus on nitrogenated derivatives, which are prevalent in the atmosphere and not as relevant in soil. Additionally, PAH products commonly found in the environment can range from as large as five rings to as small as a single ring, requiring a broadly inclusive methodology to comprehensively evaluate mutagenic potential. We developed a combination of supervised and unsupervised machine learning methods to predict environmentally induced PAH mutagenicity with improved performance over currently available tools. K-means clustering with principal component analysis allows us to identify molecular clusters that we hypothesize to have similar mechanisms of action. Recursive feature elimination identifies the most influential descriptors. The cluster-specific regression outperforms available classifiers in predicting direct-acting mutagens resulting from the microbial biodegradation of PAHs and provides direction for future studies evaluating the environmental hazards resulting from PAH biodegradation.

Robust classification-based molecular modelling of diverse chemical entities as potential SARS-CoV-2 3CLpro inhibitors: theoretical justification in light of experimental evidences

COVID-19 is the most unanticipated incidence of 2020 affecting the human population worldwide. Currently, it is utmost important to produce novel small molecule anti-SARS-CoV-2 drugs urgently that can save human lives globally. Based on the earlier SARS-CoV and MERS-CoV infection along with the general characters of coronaviral replication, a number of drug molecules have been proposed. However, one of the major limitations is the lack of experimental observations with different drug molecules. In this article, 70 diverse chemicals having experimental SARS-CoV-2 3CL^proinhibitory activity were accounted for robust classification-based QSAR analysis statistically validated with 4 different methodologies to recognize the crucial structural features responsible for imparting the activity. Results obtained from all these methodologies supported and validated each other. Important observations obtained from these analyses were also justified with the ligand-bound crystal structure of SARS-CoV-2 3CL^pro enzyme. Our results suggest that molecules should contain a 2-oxopyrrolidine scaffold as well as a methylene (hydroxy) sulphonic acid warhead in proper orientation to achieve higher inhibitory potency against SARS-CoV-2 3CL^pro. Outcomes of our study may be able to design and discover highly effective SARS-CoV-2 3CL^pro inhibitors as potential anticoronaviral therapy to crusade against COVID-19.

5-Arylidene(chromenyl-methylene)-thiazolidinediones: Potential New Agents against Mutant Oncoproteins K-Ras, N-Ras and B-Raf in Colorectal Cancer and Melanoma

Background and objectives: Cancer represents the miscommunication between and within the body cells. The mutations of the oncogenes encoding the MAPK pathways play an important role in the development of tumoral diseases. The mutations of KRAS and BRAF oncogenes are involved in colorectal cancer and melanoma, while the NRAS mutations are associated with melanoma. Thiazolidine-2,4-dione is a versatile scaffold in medicinal chemistry and a useful tool in the development of new antitumoral compounds. The aim of our study was to predict the pharmacokinetic/pharmacodynamic properties, the drug-likeness and lead-likeness of two series of synthetic 5-arylidene(chromenyl-methylene)-thiazolidinediones, the molecular docking on the oncoproteins K-Ras, N-Ras and B-Raf, and to investigate the cytotoxicity of the compounds, in order to select the best structural profile for potential anticancer agents. Materials and Methods: In our paper we studied the cytotoxicity of two series of thiazolidine-2,4-dione derivatives, their ADME-Tox properties and the molecular docking on a mutant protein of K-Ras, two isoforms of N-Ras and an isoform of B-Raf with 16 mutations. Results: The heterocyclic compounds strongly interact with K-Ras and N-Ras right after their posttranslational processing and/or compete with GDP for the nucleotide-binding site of the two GTPases. They are less active against the GDP-bound states of the two targets. All derivatives have a similar binding pattern in the active site of B-Raf. Conclusions: The data obtained encourage the further investigation of the 5-arylidene(chromenyl-methylene)-thiazolidinediones as potential new agents against the oncoproteins K-Ras, N-Ras and B-Raf.

Design, Synthesis and Antifungal Activity Evaluation of New Thiazolin-4-ones as Potential Lanosterol 14α-Demethylase Inhibitors

Twenty-three thiazolin-4-ones were synthesized starting from phenylthioamide or thiourea derivatives by condensation with α-monochloroacetic acid or ethyl α-bromoacetate, followed by substitution in position 5 with various arylidene moieties. All the synthesized compounds were physico-chemically characterized and the IR (infrared spectra), ¹H NMR (proton nuclear magnetic resonance), ¹³C NMR (carbon nuclear magnetic resonance) and MS (mass spectrometry) data were consistent with the assigned structures. The synthesized thiazolin-4-one derivatives were tested for antifungal properties against several strains of Candida and all compounds exhibited efficient anti-Candida activity, two of them (9b and 10) being over 500-fold more active than fluconazole. Furthermore, the compounds' lipophilicity was assessed and the compounds were subjected to in silico screening for prediction of their ADME-Tox properties (absorbtion, distribution, metabolism, excretion and toxicity). Molecular docking studies were performed to investigate the mode of action towards the fungal lanosterol 14α-demethylase, a cytochrome P450-dependent enzyme. The results of the in vitro antifungal activity screening, docking study and ADME-Tox prediction revealed that the synthesized compounds are potential anti-Candida agents that might act by inhibiting the fungal lanosterol 14α-demethylase and can be further optimized and developed as lead compounds.

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.

2-Arylpaullones are selective antitrypanosomal agents

Antileishmanial paullone-chalcone hybrid molecules display antiparasitic activity against Trypanosoma brucei rhodesiense blood stream forms, albeit with low selectivity against human THP-1 cells. In order to develop less toxic analogues, paullones with acrylamide or aryl substituents in 2-position were synthesized, of which the latter exhibited potent antiparasitic activity with excellent selectivity profiles. The most potent compound identified in this study was 9-tert-butyl-2-(4-morpholinophenyl)paullone (3i) which inhibited the parasites at submicromolar concentrations (GI50 = 510 nM) with a selectivity index of 157.

Mutagenicity, carcinogenicity, and other end points

Aiming at understanding the structural and physical chemical basis of the biological activity of chemicals, the science of structure-activity relationships has seen dramatic progress in the last decades. Coarse-grain, qualitative approaches (e.g., the structural alerts), and fine-tuned quantitative structure-activity relationship models have been developed and used to predict the toxicological properties of untested chemicals. More recently, a number of approaches and concepts have been developed as support to, and corollary of, the structure-activity methods. These approaches (e.g., chemical relational databases, expert systems, software tools for manipulating the chemical information) have dramatically expanded the reach of the structure-activity work; at present, they are powerful and inescapable tools for computer chemists, toxicologists, and regulators. This chapter, after a general overview of traditional and well-known approaches, gives a detailed presentation of the latter more recent support tools freely available in the public domain.

QSAR study of liver specificity of carcinogenicity of N-nitroso compounds

The quantitative structure-activity relationship (QSAR) of N-nitroso compounds (NOCs) for rat liver was developed by a topological sub-structural molecular-descriptors (TOPS-MODE) approach to predict non-liver-carcinogenic and liver-carcinogenic N-nitroso compounds based on a data set of 108 NOCs. Three descriptors calculated solely from the molecular structures of the compounds were selected by enhanced replacement method (ERM) and were weighted, respectively, with atomic weight, bond dipole moments and Abraham solute descriptor partition between water and aqueous solvent systems to indicate the importance of their roles in liver specificity. A detailed discussion on these three descriptors was carried out, and the contributions of different fragments to rat-liver specificity and the interactions among fragments were analyzed. Such results can offer some useful theoretical references for understanding the chemical structural and biological factors related to the liver-specific biological activity of NOCs.

Chemoinformatics and chemical genomics: potential utility of in silico methods

Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

The toxicological assessment of genotoxic impurities is important in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure-Activity Relationships (QSARs), Structure-Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or used practically by various regulatory agencies (e.g. US Food and Drug Administration, US and Danish Environmental Protection Agencies), as well as other existing programs were analysed. Both statistically based and knowledge-based (expert system) tools were analysed. The overall conclusions on the available in silico tools for genotoxicity and carcinogenicity prediction are quite optimistic, and the regulatory application of QSAR methods is constantly growing. For regulatory purposes, it is recommended that predictions of genotoxicity/carcinogenicity should be based on a battery of models, combining high-sensitivity models (low rate of false negatives) with high-specificity ones (low rate of false positives) and in vitro assays in an integrated manner.

Predicting carcinogenicity and understanding the carcinogenic mechanism of N-nitroso compounds using a TOPS-MODE approach

A linear discriminant analysis (LDA) coupled with an enhanced replacement method (ERM) was used as an alternative method to predict the carcinogenicity of N-nitroso compounds (NOCs) in rats. This presented LDA based on the topological substructural molecular descriptors (TOPS-MODE) approach was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 111 NOCs with a good classification value of 90.1%. The predictive power of the LDA model was validated through an external validation set (37 compounds) with a prediction accuracy of 94.6% and a leave-one-out cross-validation procedure (LOOCV) with a good prediction of 86.5%. This methodology showed that the TOPS-MODE descriptors weighted, respectively, by bond dipole moment and Abraham solute descriptor dipolarity/polarizability affected the NOC carcinogenicity. The contributions of certain bonds and fragments to carcinogenicity were used to assess biotransformation and carcinogenic mechanisms. The positive contribution of the carbon-nitrogen single bond (between the N-nitroso group and α-carbon to the N-nitroso group) indicated that the α-hydroxylation reaction could occur at the α-carbon or otherwise not occur. Similarly, the contributions from the molecular fragment could be applied to indicate whether the fragments generated an alkylating agent. These results suggested that this approach could discriminate between carcinogenic and noncarcinogenic NOCs, thereby providing insight into the structural features and chemical factors related to NOC carcinogenicity.

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).