QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

Vanilla from Brazilian Atlantic Forest: In vitro and in silico toxicity assessment and high-resolution metabolomic analysis of Vanilla spp. ethanolic extracts

The natural vanilla market, which generates millions annually, is predominantly dependent on Vanilla planifolia, a species characterized by low genetic variability and susceptibility to pathogens. There is an increasing demand for natural vanilla, prized for its complex, authentic, and superior quality compared to artificial counterparts. Therefore, there is a necessity for innovative production alternatives to ensure a consistent and stable supply of vanilla flavors. In this context, vanilla crop wild relatives (WRs) emerge as promising natural sources of the spice. However, these novel species must undergo toxicity assessments to evaluate potential risks and ensure safety for consumption. This study aimed to assess the non-mutagenic and non-carcinogenic properties of ethanolic extracts from V. bahiana, V. chamissonis, V. cribbiana, and V. planifolia through integrated metabolomic profiling, in vitro toxicity assays, and in silico analyses. The integrated approach of metabolomics, in vitro assays, and in silico analyses has highlighted the need for further safety assessments of Vanilla cribbiana ethanolic extract. While the extracts of V. bahiana, V. chamissonis, and V. planifolia generally demonstrated non-mutagenic properties in the Ames assay, V. cribbiana exhibited mutagenicity at high concentrations (5000 μg/plate) in the TA98 strain without metabolic activation. This finding, coupled with the dose-dependent cytotoxicity observed in WST-1 (Water Soluble Tetrazolium) assays, a colorimetric method that assesses the viability of cells exposed to a test substance, underscores the importance of concentration in the safety evaluation of these extracts. Kaempferol and pyrogallol, identified with higher intensity in V. cribbiana, are potential candidates for in vitro mutagenicity. Although the results are not conclusive, they suggest the safety of these extracts at low concentrations. This study emphasizes the value of an integrated approach in providing a nuanced understanding of the safety profiles of natural products, advocating for cautious use and further research into V. cribbiana mutagenicity.

In silico prediction of the mutagenicity of nitroaromatic compounds using correlation weights of fragments of local symmetry

Most quantitative structure-property/activity relationships (QSPRs/QSARs) techniques involve using different programs separately for generating molecular descriptors and separately for building models based on available descriptors. Here, the capabilities of the CORAL program are evaluated. A user of the program should apply as the basis for models the representation of the molecular structure by means of the simplified molecular input-line entry system (SMILES) as well as experimental data on the endpoint of interest. The local symmetry of SMILES is a novel composition of symmetrically represented symbols, which are three 'xyx', four 'xyyx', or five symbols 'xyzyx'. We updated our CORAL software using this optimal, new flexible descriptor, sensitive to the symmetric composition of a specific part of the molecule. Computational experiments have shown that taking account of these attributes of SMILES can improve the predictive potential of models for the mutagenicity of nitroaromatic compounds. In addition, the above computational experiments have confirmed the advantage of using the index of ideality of correlation (IIC) and the correlation intensity index (CII) for Monte Carlo optimization of the correlation weights for various attributes of SMILES, including the local symmetry. The average value of the coefficient of determination for the validation set (five different models) without fragments of local symmetry is 0.8589 ± 0.025, whereas using fragments of local symmetry improves this criterion of the predictive potential up to 0.9055 ± 0.010.

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.

QSPR study on the polyacrylate-water partition coefficients of hydrophobic organic compounds

The partition coefficient is essential for the analysis of organic chemicals using solid-phase microextraction (SPME) techniques. In this study, a quantitative structure-property relationship (QSPR) model was developed with chemical descriptors for the prediction of the polyacrylate (PA)-water partition coefficient (K_PA-w). The major variables influencing K_PA-w in the QSPR model were CrippenlogP (crippen octanal-water partition coefficient), RNCG (relative negative charge-most negative charge/total negative charge), VE2_Dzv (average coefficient sum of the last eigenvector from the Barysz matrix/weighted by van der Waals volume), and ATSC4v (centred Broto-Moreau autocorrelation-lag 4/weighted by van der Waals volume). The relative determination coefficient (R²) and cross-validation coefficient (Q²) were 0.898 and 0.858, respectively, which implied that the model had excellent robustness. Mechanistic interpretation suggested that the factors affecting the partitioning process between PA and water are the hydrophobicity, relative negative charge, and van der Waals volume of a chemical. The results of this study provide a good tool for predicting the log K_PA-w values of diverse hydrophobic organic compounds (HOCs) within the applicability domain to reduce experimental costs and the time required for innovation.

Considerations for the Use of Mutation as a Regulatory Endpoint in Risk Assessment

Assessment of a chemical's potential to cause permanent changes in the genetic code has been a common practice in the industry and regulatory settings for decades. Furthermore, the genetic toxicity battery of tests has typically been employed during the earliest stages of the research and development programs of new product development. A positive outcome from such battery has a major impact on the chemical's utility, industrial hygiene, product stewardship practices, and product life cycle analysis, among many other decisions that need to be taken by the industry, even before the registration of a chemical is undertaken. Under the prevailing regulatory paradigm, the dichotomous (yes/no) evaluation of the chemical's genotoxic potential leads to a conservative, linear no-threshold (LNT) risk assessment, unless compelling and undeniable data to the contrary can be provided to satisfy regulators, typically in a number of different global jurisdictions. With the current advent of predictive methods, new testing paradigms, mode-of-action/adverse outcome pathways, and quantitative risk assessment approaches, various stakeholders are starting to employ these state-of-the-science methodologies to further the conversation on decision making and advance the regulatory paradigm beyond the dominant LNT status quo. This commentary describes these novel methodologies, relevant biological responses, and how these can affect internal and regulatory risk assessment approaches. Environ. Mol. Mutagen. 61:84-93, 2020. © 2019 Wiley Periodicals, Inc.

Neonicotinoid insecticide design: molecular docking, multiple chemometric approaches, and toxicity relationship with Cowpea aphids

Neonicotinoids are the fastest-growing class of insecticides successfully applied in plant protection, human and animal health care. The significant resistance increases led to the urgent need for alternative new neonicotinoids, with improved insecticidal activity. We performed molecular docking to describe a common binding mode of neonicotinoids into the nicotinic acetylcholine receptor, and to select the appropriate conformations to derive models. These were further used in a QSAR study employing both linear and nonlinear approaches to model the inhibitory activity against the Cowpea aphids. Linear modeling was performed by multiple linear regression and partial least squares and nonlinear modeling by artificial neural networks and support vector machine methods. The OECD principles were considered for QSAR models validation. Robust models with predictive power were found for neonicotinoid diverse structures. Based on our QSAR and docking outcomes, five new insecticides were predicted, according to the model applicability domain, the ligand efficiencies, and the binding mode. Therefore, the developed models can be confidently used for the prediction of the insecticidal activity of new chemicals, saving a substantial amount of time and money and, also, contributing to the chemical risk assessment.

In silico model for mutagenicity (Ames test), taking into account metabolism

Bacterial reverse mutation test is one of the most common methods used to address genotoxicity. The experimental test is designed to include a step to simulate mammalian metabolism. The most common metabolic activation system is the incubation with S9 fraction prepared from the livers of rodents. Usually, in silico models addressing this endpoint are developed on the basis of an overall call disregarding the fact that the toxic effect was observed before or after metabolic activation. Here, we present a new in silico model to predict mutagenicity as measured by activity in the bacterial reverse mutation test, bearing in mind the role of S9 activation to stimulate metabolism. We applied the software SARpy, which identifies structural alerts associated with the effect. Different rules codified by these structural alerts were found in case of positive or negative mutagenicity, observed in the presence or absence of the S9 fraction. These rules can be used to understand the role of metabolism in mutagenicity better. We also identified a possible association of the results from these models with carcinogenicity.

QSAR model for predicting the toxicity of organic compounds to fathead minnow

In this work, a new norm descriptor is proposed based on atomic properties. A quantitative structure-activity relationship (QSAR) model for predicting the toxicity of organic compounds to fathead minnow is further developed by norm descriptors. Results indicate that this new model based on the norm descriptors has satisfactory predictive results with the squared correlation coefficient (R²) and squared relation coefficient of the cross validation (Q²) of 0.8174 and 0.7923, respectively. Combining with Y-randomization test, applicability domain test, and comparison with other references, calculation results indicate that the QSAR model performs well both in the stability and the accuracy with wide application domain, which might be further used effectively for the safe and risk assessment of various organics.

Modelling the water-plant cuticular polymer matrix membrane partitioning of diverse chemicals in multiple plant species using the support vector machine-based QSAR approach

In this study, a support vector machine (SVM) based multi-species QSAR (quantitative structure-activity relationship) model was developed for predicting the water-plant cuticular polymer matrix membrane (MX) partition coefficient, K_MXw of diverse chemicals using two simple molecular descriptors derived from the chemical structures and following the OECD guidelines. Accordingly, the Lycopersicon esculentum Mill. data were used to construct the QSAR model that was externally validated using three other plant species data. The diversity in chemical structures and end-points were verified using the Tanimoto similarity index and Kruskal-Wallis statistics. The predictive power of the developed QSAR model was tested through rigorous validation, deriving a wide series of statistical checks. The MLOGP was the most influential descriptor identified by the model. The model yielded a correlation (r²) of 0.966 and 0.965 in the training and test data arrays. The developed QSAR model also performed well in another three plant species (r² > 0.955). The results suggest the appropriateness of the developed model to reliably predict the plant chemical interactions in multiple plant species and it can be a useful tool in screening the new chemical for environmental risk assessment.

Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results

Quantitative structure-activity relationships (QSARs) for prediction of toxicological endpoints built up with the CORAL software are discussed. Prejudices related to these QSAR models are listed. Possible ways to improve the software are discussed.