Evaluation of new alternative methods for the identification of estrogenic, androgenic and steroidogenic effects: a comparative in vitro/in silico study

A suite of in vitro assays and in silico models were evaluated to identify which best detected the endocrine-disrupting (ED) potential of 10 test chemicals according to their estrogenic, androgenic and steroidogenic (EAS) potential compared to the outcomes from ToxCast. In vitro methods included receptor-binding, CALUX transactivation, H295R steroidogenesis, aromatase activity inhibition and the Yeast oestrogen (YES) and Yeast androgen screen (YAS) assays. The impact of metabolism was also evaluated. The YES/YAS assays exhibited a high sensitivity for ER effects and, despite some challenges in predicting AR effects, is a good initial screening assay. Results from receptor-binding and CALUX assays generally correlated and were in accordance with classifications based on ToxCast assays. ER agonism and AR antagonism of benzyl butyl phthalate were abolished when CALUX assays included liver S9. In silico final calls were mostly in agreement with the in vitro assays, and predicted ER and AR effects well. The efficiency of the in silico models (reflecting applicability domains or inconclusive results) was 43-100%. The percentage of correct calls for ER (50-100%), AR (57-100%) and aromatase (33-100%) effects when compared to the final ToxCast call covered a wide range from highly reliable to less reliable models. In conclusion, Danish (Q)SAR, Opera, ADMET Lab LBD and ProToxII models demonstrated the best overall performance for ER and AR effects. These can be combined with the YES/YAS assays in an initial screen of chemicals in the early tiers of an NGRA to inform on the MoA and the design of mechanistic in vitro assays used later in the assessment. Inhibition of aromatase was best predicted by the Vega, AdmetLab and ProToxII models. Other mechanisms and exposure should be considered when making a conclusion with respect to ED effects.

A local QSAR model based on the stability of nitrenium ions to support the ICH M7 expert review on the mutagenicity of primary aromatic amines

BACKGROUND

Aromatic amines, often used as intermediates for pharmaceutical synthesis, may be mutagenic and therefore pose a challenge as metabolites or impurities in drug development. However, predicting the mutagenicity of aromatic amines using commercially available, quantitative structure-activity relationship (QSAR) tools is difficult and often requires expert review. In this study, we developed a shareable QSAR tool based on nitrenium ion stability.

RESULTS

The evaluation using in-house aromatic amine intermediates revealed that our model has prediction accuracy of aromatic amine mutagenicity comparable to that of commercial QSAR tools. The effect of changing the number and position of substituents on the mutagenicity of aromatic amines was successfully explained by the change in the nitrenium ion stability. Furthermore, case studies showed that our QSAR tool can support the expert review with quantitative indicators.

CONCLUSIONS

This local QSAR tool will be useful as a quantitative support tool to explain the substituent effects on the mutagenicity of primary aromatic amines. By further refinement through method sharing and standardization, our tool can support the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 expert review with quantitative indicators.

Revisiting Thin-Layer Electrochemistry in a Chip-Type Cell for the Study of Electro-organic Reactions

It is important but challenging to elucidate the electrochemical reaction mechanisms of organic compounds using electroanalytical methods. Particularly, a rapid and straightforward method that provides information on reaction intermediates or other key electrochemical parameters may be useful. In this work, we exploited the advantages of classic thin-layer electrochemistry to develop a thin-layer electroanalysis microchip (TEAM). The TEAM provided better-resolved voltammetric peaks than under semi-infinite diffusion conditions owing to its small height. Importantly, rapid and accurate determination of the number of electrons transferred, n, was enabled by mechanically confining the microliter-scale volume analyte at the electrode, while securing ionic conduction using polyelectrolyte gels. The performance of the TEAM was validated using voltammetry and coulometry of standard redox couples. Utilizing the TEAM, a (spectro)electrochemical analysis of FM 1-43, an organic dye widely used in neuroscience, was successfully performed. Moreover, the TEAM was applied to study the electrochemical oxidation mechanism of pivanilides and alkyltrifluoroborate salts with different substituents and solvents. This work suggests that TEAM is a promising tool to provide invaluable mechanistic information and promote the rational design of electrosynthetic strategies.

New in silico models to predict in vitro micronucleus induction as marker of genotoxicity

The evaluation of genotoxicity is a fundamental part of the safety assessment of chemicals due to the relevance of the potential health effects of genotoxicants. Among the testing methods available, the in vitro micronucleus assay with mammalian cells is one of the most used and required by regulations targeting several industrial sectors such as the cosmetic industry and food-related sectors. As an alternative to the testing methods, in recent years, lots in silico methods were developed to predict the genotoxicity of chemicals, including models for the Ames mutagenicity test, the in vitro chromosomal aberrations and the in vivo micronucleus assay. We developed several in silico models for the prediction of genotoxicity as reflected by the in vitro micronucleus assay. The resulting models include both statistical and knowledge-based models. The most promising model is the one based on fragments extracted with the SARpy platform. More than 100 structural alerts were extracted, including also fragments associated with the non-genotoxic activity. The model is characterized by high accuracy and the lowest false negative rate, making this tool suitable for chemical screening according to the regulators' needs. The SARpy model will be implemented on the VEGA platform (https://www.vegahub.eu) and will be freely available.

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.

In silico prediction of chromosome damage: comparison of three (Q)SAR models

Two major endpoints for genotoxicity tests are gene mutation and chromosome damage (CD), which includes clastogenicity and aneugenicity detected by chromosomal aberration (CA) test or micronucleus (MN) test. Many in silico prediction systems for bacterial mutagenicity (i.e. Ames test results) have been developed and marketed. They show good performance for prediction of Ames mutagenicity. On the other hand, it seems that in silico prediction of CD does not progress as much as Ames prediction. Reasons for this include different mechanisms and detection methods, many false positives and conflicting test results. However, some (quantitative) structure-activity relationship ((Q)SAR) models (e.g. Derek Nexus [Derek], ADMEWorks [AWorks] and CASE Ultra [MCase]) can predict CA test results. Therefore, performances of the three (Q)SAR models were compared using the expanded Carcinogenicity Genotoxicity eXperience (CGX) dataset for understanding current situations and future development. The constructed dataset contained 440 chemicals (325 carcinogens and 115 non-carcinogens). Sensitivity, specificity, accuracy or applicability of each model were 56.0, 86.9, 68.6 or 89.1% in Derek, 67.7, 61.5, 65.2 or 99.3% in AWorks, and 91.0, 64.9, 80.5 or 97.7% in MCase, respectively. The performances (sensitivity and accuracy) of MCase were higher than those of Derek or AWorks. Analysis of predictivity of (Q)SAR models of certain chemical classes revealed no remarkable differences among the models. The tendency of positive prediction by (Q)SAR models was observed in alkylating agents, aromatic amines or amides, aromatic nitro compounds, epoxides, halides and N-nitro or N-nitroso compounds. In an additional investigation, high sensitivity but low specificity was noted in in vivo MN prediction by MCase. Refinement of test data to be used for in silico system (e.g. consideration of cytotoxicity or re-evaluation of conflicting test results) will be needed to improve performance of CD prediction.

In silico prediction of chemical genotoxicity using machine learning methods and structural alerts

Genotoxicity tests can detect compounds that have an adverse effect on the process of heredity. The in vivo micronucleus assay, a genotoxicity test method, has been widely used to evaluate the presence and extent of chromosomal damage in human beings. Due to the high cost and laboriousness of experimental tests, computational approaches for predicting genotoxicity based on chemical structures and properties are recognized as an alternative. In this study, a dataset containing 641 diverse chemicals was collected and the molecules were represented by both fingerprints and molecular descriptors. Then classification models were constructed by six machine learning methods, including the support vector machine (SVM), naïve Bayes (NB), k-nearest neighbor (kNN), C4.5 decision tree (DT), random forest (RF) and artificial neural network (ANN). The performance of the models was estimated by five-fold cross-validation and an external validation set. The top ten models showed excellent performance for the external validation with accuracies ranging from 0.846 to 0.938, among which models Pubchem_SVM and MACCS_RF showed a more reliable predictive ability. The applicability domain was also defined to distinguish favorable predictions from unfavorable ones. Finally, ten structural fragments which can be used to assess the genotoxicity potential of a chemical were identified by using information gain and structural fragment frequency analysis. Our models might be helpful for the initial screening of potential genotoxic compounds.

Insights into the Mechanism of Anodic N-N Bond Formation by Dehydrogenative Coupling

The electrochemical synthesis of pyrazolidine-3,5-diones and benzoxazoles by N-N bond formation and C,O linkage, respectively, represents an easy access to medicinally relevant structures. Electrochemistry as a key technology ensures a safe and sustainable approach. We gained insights in the mechanism of these reactions by combining cyclovoltammetric and synthetic studies. The electron-transfer behavior of anilides and dianilides was studied and led to the following conclusion: The N-N bond formation involves a diradical as intermediate, whereas the benzoxazole formation is based on a cationic mechanism. Besides these studies, we developed a synthetic route to mixed dianilides as starting materials for the N-N coupling. The compatibility with valuable functionalities like triflates and mesylates for follow-up reactions as well as the comparison of different electrochemical set-ups also enhanced the applicability of this method.

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties

Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g., the European Union's Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database, and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural, and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT) approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (< 5). We discuss the limitations of the approach. Based on this analysis we propose improvements for a follow-up approach, such as incorporation of metabolic information and more detailed mechanistic information. The software enables the user to allocate a substance in a cluster and to use this information for a possible read- across. The clustering tool is provided as a free web service, accessible at http://mlc-reach.informatik.uni-mainz.de.