The VERA software: Implementation of the acute fish toxicity endpoint and its application to pharmaceutical compounds.

The Virtual Extensive Read-Across software (VERA) is a new tool for read-across using a global similarity score, molecular groups, and structural alerts to find clusters of similar substances; these clusters are then used to identify suitable similar substances and make an assessment for the target substance. A beta version of VERA GUI is free and available at vegahub.eu; the source code of the VERA algorithm is available on GitHub. In the past we described its use to assess carcinogenicity, a classification endpoint. The aim here is to extend the automated read-across approach to assess continuous endpoints as well. We addressed acute fish toxicity. VERA evaluation on the acute fish toxicity endpoint was done on a dataset containing general substances (pesticides, industrial products, biocides, etc.), obtaining an overall R2 of 0.68. We employed the VERA algorithm also on active pharmaceutical ingredients (APIs). We included a portion of the APIs in the training dataset to predict APIs, successfully achieving an overall R2 of 0.63. VERA evaluates the assessment's reliability, and we reached an R2 of 0.78 and Root Mean Square Error (RMSE) of 0.44 for predictions with high reliability.

Virtual Extensive Read-Across: A New Open-Access Software for Chemical Read-Across and Its Application to the Carcinogenicity Assessment of Botanicals.

Read-across applies the principle of similarity to identify the most similar substances to represent a given target substance in data-poor situations. However, differences between the target and the source substances exist. The present study aims to screen and assess the effect of the key components in a molecule which may escape the evaluation for read-across based only on the most similar substance(s) using a new open-access software: Virtual Extensive Read-Across (VERA). VERA provides a means to assess similarity between chemicals using structural alerts specific to the property, pre-defined molecular groups and structural similarity. The software finds the most similar compounds with a certain feature, e.g., structural alerts and molecular groups, and provides clusters of similar substances while comparing these similar substances within different clusters. Carcinogenicity is a complex endpoint with several mechanisms, requiring resource intensive experimental bioassays and a large number of animals; as such, the use of read-across as part of new approach methodologies would support carcinogenicity assessment. To test the VERA software, carcinogenicity was selected as the endpoint of interest for a range of botanicals. VERA correctly labelled 70% of the botanicals, indicating the most similar substances and the main features associated with carcinogenicity.

QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors.

Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure-activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r2 values of 0.57 and 0.65 in the regression models for oral and inhalation SFs in external validation. These models might therefore support regulators in (de)prioritizing substances for regulatory action and in weighing evidence in the context of chemical safety assessments. Moreover, these models are implemented on the VEGA platform and are now freely downloadable online.

Integrating computational methods to predict mutagenicity of aromatic azo compounds.

Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes. A benchmark dataset of Ames data for 354 azo dyes was employed to develop three classification strategies using knowledge-based methods and docking simulations. Results were compared and integrated with three models from the literature, developing a series of consensus strategies. The good results confirm the usefulness of in silico methods as a support for experimental methods to predict the mutagenicity of azo compounds.

A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds.

Cancer is one of the main causes of death in Western countries, and a major issue for human health. Prolonged exposure to a number of chemicals was observed to be one of the primary causes of cancer in occupationally exposed persons. Thus, the development of tools for identifying hazardous chemicals and the increase of mechanistic understanding of their toxicity is a major goal for scientific research. We constructed a new knowledge-based expert system accounting the effect of different substituents for the prediction of mutagenicity (Ames test) of aromatic amines, a class of compounds of major concern because of their widespread application in industry. The herein presented model implements a series of user-defined structural rules extracted from a database of 616 primary aromatic amines, with their Ames test outcomes, aimed at identifying mutagenic and non-mutagenic chemicals. The chemical rationale behind such rules is discussed. Besides assessing the model's ability to correctly classify aromatic amines, its predictivity was further evaluated on a second database of 354 azo dyes, another class of chemicals of major concern, whose toxicity has been predicted on the basis of the toxicity of aromatic amines potentially generated from the metabolic reduction of the azo bond. Good performance in classification on both the amine (MCC, Matthews Correlation Coefficient=0.743) and the azo dye (MCC=0.584) datasets confirmed the predictive power of the model, and its suitability for use on a wide range of chemicals. Finally, the model was compared with a series of well-known mutagenicity predicting software. The good performance of our model compared with other mutagenicity models, especially in predicting azo dyes, confirmed the usefulness of this expert system as a reliable support to in vitro mutagenicity assays for screening and prioritization purposes. The model has been fully implemented as a KNIME workflow and is freely available for downstream users.

New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds.

In this study, new molecular fragments associated with genotoxic and nongenotoxic carcinogens are introduced to estimate the carcinogenic potential of compounds. Two rule-based carcinogenesis models were developed with the aid of SARpy: model R (from rodents' experimental data) and model E (from human carcinogenicity data). Structural alert extraction method of SARpy uses a completely automated and unbiased manner with statistical significance. The carcinogenicity models developed in this study are collections of carcinogenic potential fragments that were extracted from two carcinogenicity databases: the ANTARES carcinogenicity dataset with information from bioassay on rats and the combination of ISSCAN and CGX datasets, which take into accounts human-based assessment. The performance of these two models was evaluated in terms of cross-validation and external validation using a 258 compound case study dataset. Combining R and H predictions and scoring a positive or negative result when both models are concordant on a prediction, increased accuracy to 72% and specificity to 79% on the external test set. The carcinogenic fragments present in the two models were compared and analyzed from the point of view of chemical class. The results of this study show that the developed rule sets will be a useful tool to identify some new structural alerts of carcinogenicity and provide effective information on the molecular structures of carcinogenic chemicals.

Hierarchical Rules for Read-Across and In Silico Models of Mutagenicity.

A broad set of rules has been implemented within the ToxRead software for read-across of chemicals for bacterial mutagenicity. These rules were obtained by manually analyzing more than 6000 chemicals and the associated chemical classes. A hierarchy of rules was established to identify those most specifically relating to the target compounds, linked in sequence to the other, more generic ones, which may match with the target compound. Rules related to both mutagenicity and lack of mutagenicity were found. Some of the latter are exceptions to the mutagenicity rules, while others are modulators of activity. These rules can also be used to predict mutagenicity, offering good performance.

coral Software: QSAR for Anticancer Agents.

CORrelations And Logic (coral at http://www.insilico.eu/coral) is freeware aimed at establishing a quantitative structure - property/activity relationships (QSPR/QSAR). Simplified molecular input line entry system (SMILES) is used to represent the molecular structure. In fact, symbols in SMILES nomenclatures are indicators of the presence of defined molecular fragments. By means of the calculation with Monte Carlo optimization of the so called correlation weights (contributions) for the above-mentioned molecular fragments, one can define optimal SMILES-based descriptors, which are correlated with an endpoint for the training set. The predictability of these descriptors for an external validation set can be estimated. A collection of SMILES-based models of anticancer activity of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines for different splits into training and validation set which are calculated with the coral are examined and discussed. Good performance has been obtained for three splits: the r(2) ranged between 0.778 and 0.829 for the sub-training set, between 0.828 and 0.933 for the calibration set, and between 0.807 and 0.931 for the validation set.