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.

The assessment of human health risks resulting from the presence of metabolites in groundwater and food residues has become an important element in pesticide authorisation. In this context, the evaluation of mutagenicity is of particular interest and a paradigm shift from exposure‐triggered testing to in silico ‐based screening has been recommended in the European Food Safety Authority (EFSA) Guidance on the establishment of the residue definition for dietary risk assessment . In addition, it is proposed to apply in silico predictions when experimental mutagenicity testing is not possible due to a lack of sufficient quantities of the pesticide metabolite. This, combined with animal welfare and economic considerations, has led to a situation where an increasing number of in silico studies are submitted to regulatory authorities. Whilst there is extensive experience with in silico predictions for mutagenicity in the chemical and pharmaceutical industry, their suitability in pesticide regulation is still insufficiently considered. Therefore, we herein discuss critical issues that need to be resolved to successfully implement (Quantitative) Structure‐Activity Relationship ((Q)SAR) as an accepted tool in pesticide regulation. For illustration purposes, the results of a pilot study are included. The presented study highlights a need for further improvement regarding the predictivity and applicability domain of (Q)SAR systems for pesticides and their metabolites, but also raises other questions such as model selection, establishment of acceptance criteria, harmonised approaches to the combination of model outputs into overall conclusions, adequate reporting and data sharing. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Experimental genotoxicity data are required for pesticidal and biocidal active substances prior to regulatory approval, while for their metabolites and impurities, in silico predictions are often accepted. Nonetheless, the extent to which these compounds are represented in publicly available genotoxicity databases remains unclear. Herein, we utilize chemical space methods to define the overlap between pesticide substances (active substances, metabolites, and impurities) and activity data for six genotoxicity test types commonly employed in regulatory toxicology: the Ames test, the in vitro mammalian cell gene mutation test, the in vitro micronucleus test, the in vitro chromosomal aberration test, the in vivo micronucleus test, and the in vivo chromosomal aberration test. After merging and performing structure standardization on 18 public pesticide/biocide databases, we identified 4826 unique substances. Within 19 public genotoxicity databases, 19,897 substances had at least one data point in at least one genotoxicity test. The chemical space overlap between the pesticide substances and each genotoxicity set was evaluated by calculating physicochemical descriptors and molecular fingerprints, which were visualized by using dimensionality reduction methods. The chemical space of pesticide substances is well represented by substances with Ames test data and, to varying degrees, by substances with data from the other genotoxicity tests, with particularly low coverage for in vivo chromosomal aberration. The major scaffolds identified in pesticide substances were present in all of the genotoxicity data sets. Compared to pesticide substances, the genotoxicity data sets were enriched in functional groups characteristic of genotoxic compounds, such as annulated rings, but depleted in pesticide-typical structural motifs like halogens. Chemical space methods can assist regulatory toxicologists in understanding regions of pesticide substance chemical space that are well- or poorly characterized by genotoxicity data. This understanding is important for the accurate and targeted use of databases and data-based nontesting methods in line with regulatory requirements.

Genotoxicity test data is required for pesticidal and biocidal active substances prior to regulatory approval, while for their metabolites and impurities, in silico predictions are often accepted. Nonetheless, the extent to which these compounds are represented in publicly available genotoxicity databases remains unclear. Herein, we utilize chemical space methods to define the overlap between pesticide substances and activity data for six genotoxicity test types commonly employed in regulatory toxicology: the Ames test, the in vitro mammalian cell gene mutation test, the in vitro micronucleus test, the in vitro chromosomal aberration test, the in vivo micronucleus test, and the in vivo chromosomal aberration test. After merging and performing structure standardization on 18 pesticide/biocide databases, we identified 4,932 unique substances. Within 16 public genotoxicity databases, 10,020 substances had at least one data point in one of the genotoxicity tests. The chemical space overlap between the pesticide substances and each genotoxicity set was evaluated by calculating physicochemical descriptors and molecular fingerprints, which were visualized using PCA and UMAP, respectively. The chemical space of pesticide substances was well represented by Ames test data and, to varying degrees, by the other genotoxicity tests, with particularly low coverage for in vivo chromosomal aberration. The major scaffolds found in pesticide substances appeared in all genotoxicity data sets. Functional groups overrepresented in the genotoxicity data compared to pesticide substances were indicative of prototypical genotoxic substances, while those that were underrepresented included motifs potentially associated with pesticides (e.g., halogens). Chemical space methods can assist regulatory toxicologists in understanding regions of pesticide substance chemical space that are well or poorly characterized by genotoxicity data. This understanding is important for the accurate and targeted use of databases and data-based non-testing methods such as QSAR and read-across in line with regulatory requirements.

Efforts to extend longevity and healthspan have benefited from the use of in vitro systems, short‐lived model organisms and computational approaches, but translation to humans depends on demonstrating efficacy and safety in mammalian in vivo models. However, rigorously controlled rodent lifespan studies are slow, costly and low‐throughput. Hormesis, the induction of adaptive stress response pathways by low‐level toxic or metabolic challenges, provides a mechanistic means for improved resilience, but testing hormetic dose–response relationships in mammals is logistically challenging. Regulatory long‐term toxicity studies, mandated for chemical authorisation, routinely generate lifespan and chronic toxicity data from large cohorts across multiple dose groups and therefore represent an underused resource for detecting unexpected survival benefits. We mined a random subset of the German Federal Institute for Risk Assessment (BfR) electronic archive for long‐term rodent pesticide studies in ≥ 50 animals per dose and sex. From qualifying studies with 154 substances, we identified 27 substances (18% of entries) that significantly increased survival at study termination (≥ 10%, p < 0.05). Fifteen publicly disclosable substances were examined further; roughly half perturbed mitochondrial oxygen consumption rates in mammalian cells, and bromoxynil acted as a mitochondrial uncoupler. Back‐translation to Caenorhabditis elegans showed bromoxynil improved multiple healthspan indicators without compromising organismal health. Regulatory toxicity archives can thus be repurposed to identify candidate compounds, targets and mechanisms for mammalian healthy ageing research, complement conventional screening pipelines, align with 3Rs by maximising existing data, and supply high‐quality datasets for AI‐driven prioritisation of interventions for rigorous preclinical follow‐up.