(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

Data-driven toxicity prediction in drug discovery: Current status and future directions

Early toxicity assessment plays a vital role in the drug discovery process on account of its significant influence on the attrition rate of candidates. Recently, constant upgrading of information technology has greatly promoted the continuous development of toxicity prediction. To give an overview of the current state of data-driven toxicity prediction, we reviewed relevant studies and summarized them in three main respects: the features and difficulties of toxicity prediction, the evolution of modeling approaches, and the available tools for toxicity prediction. For each part, we expound the research status, existing challenges, and feasible solutions. Finally, several new directions and suggestions for toxicity prediction are also put forward.

Matrine and Oxymatrine: evaluating the gene mutation potential using in silico tools and the bacterial reverse mutation assay (Ames test)

The quinolizidine alkaloids matrine and its N-oxide oxymatrine occur in plants of the genus Sophora. Recently, matrine was sporadically detected in liquorice products. Morphological similarity of the liquorice plant Glycyrrhiza glabra with Sophora species and resulting confusion during harvesting may explain this contamination, but use of matrine as pesticide has also been reported. The detection of matrine in liquorice products raised concern as some studies suggested a genotoxic activity of matrine and oxymatrine. However, these studies are fraught with uncertainties, putting the reliability and robustness into question. Another issue was that Sophora root extracts were usually tested instead of pure matrine and oxymatrine. The aim of this work was therefore to determine whether matrine and oxymatrine have potential for causing gene mutations. In a first step and to support a weight-of-evidence analysis, in silico predictions were performed to improve the database using expert and statistical systems by VEGA, Leadscope (Instem®), and Nexus (Lhasa Limited). Unfortunately, the confidence levels of the predictions were insufficient to either identify or exclude a mutagenic potential. Thus, in order to obtain reliable results, the bacterial reverse mutation assay (Ames test) was carried out in accordance with OECD Test Guideline 471. The test set included the plate incorporation and the preincubation assay. It was performed with five different bacterial strains in the presence or absence of metabolic activation. Neither matrine nor oxymatrine induced a significant increase in the number of revertants under any of the selected experimental conditions. Overall, it can be concluded that matrine and oxymatrine are unlikely to have a gene mutation potential. Any positive findings with Sophora extracts in the Ames test may be related to other components. Notably, the results also indicated a need to extend the application domain of respective (Q)SAR tools to secondary plant metabolites.

Multitask Deep Neural Networks for Ames Mutagenicity Prediction

The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of Salmonella typhimurium, the vast majority of the published in silico models for predicting mutagenicity do not take into account the test results of the individual experiments conducted for each strain. Instead, such QSAR models are generally trained employing overall labels (i.e., mutagenic and nonmutagenic). Recently, neural-based models combined with multitask learning strategies have yielded interesting results in different domains, given their capabilities to model multitarget functions. In this scenario, we propose a novel neural-based QSAR model to predict mutagenicity that leverages experimental results from different strains involved in the Ames test by means of a multitask learning approach. To the best of our knowledge, the modeling strategy hereby proposed has not been applied to model Ames mutagenicity previously. The results yielded by our model surpass those obtained by single-task modeling strategies, such as models that predict the overall Ames label or ensemble models built from individual strains. For reproducibility and accessibility purposes, all source code and datasets used in our experiments are publicly available.

Use of transcriptomics in hazard identification and next generation risk assessment: A case study with clothianidin

Toxicological risk assessment is essential in the evaluation and authorization of different classes of chemical substances. Genotoxicity and mutagenicity testing are of highest priority and rely on established in vitro systems with bacterial and mammalian cells, sometimes followed by in vivo testing using rodent animal models. Transcriptomic approaches have recently also shown their value to determine transcript signatures specific for genotoxicity. Here, we studied how transcriptomic data, in combination with in vitro tests with human cells, can be used for the identification of genotoxic properties of test compounds. To this end, we used liver samples from a 28-day oral toxicity study in rats with the pesticidal active substances imazalil, thiacloprid, and clothianidin, a neonicotinoid-type insecticide with, amongst others, known hepatotoxic properties. Transcriptomic results were bioinformatically evaluated and pointed towards a genotoxic potential of clothianidin. In vitro Comet and γH2AX assays in human HepaRG hepatoma cells, complemented by in silico analyses of mutagenicity, were conducted as follow-up experiments to check if the genotoxicity alert from the transcriptomic study is in line with results from a battery of guideline genotoxicity studies. Our results illustrate the combined use of toxicogenomics, classic toxicological data and new approach methods in risk assessment. By means of a weight-of-evidence decision, we conclude that clothianidin does most likely not pose genotoxic risks to humans.

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Analysis of clothianidin genotoxicity in silico, in vitro and in vivo.

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Application of a toxicogenomics approach to analyze genotoxicity.

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Weight-of-evidence decision supports classification as “non-genotoxic”.

A critical review of effect modeling for ecological risk assessment of plant protection products

A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).

Alkenylbenzenes in Foods: Aspects Impeding the Evaluation of Adverse Health Effects

Alkenylbenzenes are naturally occurring secondary plant metabolites, primarily present in different herbs and spices, such as basil or fennel seeds. Thus, alkenylbenzenes, such as safrole, methyleugenol, and estragole, can be found in different foods, whenever these herbs and spices (or extracts thereof) are used for food production. In particular, essential oils or other food products derived from the aforementioned herbs and spices, such as basil-containing pesto or plant food supplements, are often characterized by a high content of alkenylbenzenes. While safrole or methyleugenol are known to be genotoxic and carcinogenic, the toxicological relevance of other alkenylbenzenes (e.g., apiol) regarding human health remains widely unclear. In this review, we will briefly summarize and discuss the current knowledge and the uncertainties impeding a conclusive evaluation of adverse effects to human health possibly resulting from consumption of foods containing alkenylbenzenes, especially focusing on the genotoxic compounds, safrole, methyleugenol, and estragole.