Transcriptomics analysis of hepatotoxicity induced by the pesticides imazalil, thiacloprid and clothianidin alone or in binary mixtures in a 28-day study in female Wistar rats

A benchmark concentration-based strategy for evaluating the combined effects of genotoxic compounds in TK6 cells

Chemical risk assessment has historically focused on single compounds, neglecting the implications of combined exposures. To bridge this gap, several methodologies, such as concentration addition (CA) and independent action (IA), have been developed. However, a systematic, consistent, and integrated approach across various legislative frameworks is still lacking. The assessment of combined effects of genotoxicants is even more challenging, as genotoxicity data are typically evaluated qualitatively, without considering the effect size. This study aimed to develop a quantitative approach for evaluating the combined effects of genotoxic compounds with both similar and dissimilar modes of action (MoA), based on the benchmark concentration (BMC) principle. A proof-of-concept study was conducted using the in vitro micronucleus (MNvit) test to examine two types of binary mixtures: ethyl methanesulfonate (EMS) and methyl methanesulfonate (MMS), which share similar MoA, and MMS and etoposide (ETP), which have dissimilar MoA. The methodology involved collecting data for individual compounds, calculating BMC values, composing mixtures with different ratios and inducing various effect levels, testing these mixtures, and comparing the experimental results with the modelled data to verify additivity. The findings indicated that for both mixtures, the experimental responses aligned with the predicted additive effects, supporting the validity of the additivity principle. This study highlights the potential of an optimized BMC-based approach as a robust framework for testing chemical mixtures. It should be adopted in future studies to evaluate a wider range of genotoxic compounds, offering a more comprehensive and quantitative strategy for assessing combined chemical exposures.

The zebrafish embryo as a model for chemically-induced steatosis: A case study with three pesticides

There is an increasing incidence and prevalence of fatty liver disease in the western world, with steatosis as the most prevalent variant. Known causes of steatosis include exposure to food-borne chemicals, and overconsumption of alcohol, carbohydrates and fat, and it is a well-known side effect of certain pharmaceuticals such as tetracycline, amiodarone and tamoxifen (drug-induced hepatic steatosis). Mechanistic knowledge on chemical-induced steatosis has greatly evolved and has been organized into adverse outcome pathways (AOPs) describing the chain of events from first molecular interaction of a substance with a biological system to the adverse outcome, intrahepatic lipid accumulation. In this study, three known steatosis-inducing pesticides (imazalil, clothianidin, and thiacloprid) were tested for their ability to induce hepatic triglyceride accumulation in the zebrafish (Danio rerio) embryo (ZFE) at 5 days post fertilization, both as single compounds and equipotent binary mixtures. The results indicate that the ZFE is very well applicable as a higher tier testing model to confirm effects in downstream key events in AOPs, that is, chemically-induced triglyceride accumulation in the whole organism and production of visible steatosis. Moreover, dose addition could be concluded for binary mixtures of substances with similar and with dissimilar modes of action.

Association between urinary neonicotinoid insecticide levels and dyslipidemia risk: A cross-sectional study in Chinese community-dwelling elderly

Experimental evidence has demonstrated that neonicotinoids (NEOs) exposure can cause lipid accumulation and increased leptin levels. However, the relationship between NEOs exposure and dyslipidemia in humans remains unclear, and the interactive effects of NEOs and their characteristic metabolites on dyslipidemia remain unknown. We detected 14 NEOs and their metabolites in urine samples of 500 individuals (236 and 264 with and without dyslipidemia, respectively) randomly selected from the baseline of the Yinchuan community-dwelling elderly cohort (Ningxia, China). The NEOs and their metabolites were widely detected in urine (87.2-99.6 %) samples, and the median levels ranged within 0.06-0.55 μg/g creatinine. The positive associations and dose-dependent relationships of thiacloprid, imidacloprid-olefin, and imidacloprid-equivalent total with dyslipidemia were validated using restricted cubic spline analysis. Mixture models revealed a positive association between the NEOs mixture and dyslipidemia risk, with urine desnitro-imidacloprid ranked as the top contributor. The Bayesian Kernel Machine Regression models showed that the NEOs mixtures were associated with increased dyslipidemia when the chemical mixtures were ≥ 25th percentile compared to their medians, and desnitro-imidacloprid and imidacloprid-olefin were the major contributors to the combined effect. Given the widespread use of NEOs and the dyslipidemia pandemic, further investigations are urgently needed to confirm our findings and elucidate the underlying mechanisms.

Mixture Math: Deciding What to Add in a Cumulative Risk Assessment

Component-based approaches for cumulative risk assessment provide an important tool for informing public health policy. While current quantitative cumulative risk assessments focus narrowly on pesticides that share a mechanism of action, growing scientific evidence supports expansion of their application to encompass stressors that target a common disease. Case studies have demonstrated dose additive effects of chemicals with different mechanisms of action on liver steatosis, craniofacial malformations, and male reproductive tract developmental disruption. Evidence also suggests that nonchemical stressors such as noise or psychosocial stress can modify effects of chemicals. Focused research attention is required before nonchemical stressors can routinely be included in quantitative cumulative risk assessments.

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”.

An approach for mixture testing and prioritization based on common kinetic groups

In light of an ever-increasing exposure to chemicals, the topic of potential mixture toxicity has gained increased attention, particularly as the toxicological toolbox to address such questions has vastly improved. Routinely toxicological risk assessments will rely on the analysis of individual compounds with mixture effects being considered only in those specific cases where co-exposure is foreseeable, for example for pesticides or food contact materials. In the field of pesticides, active substances are summarized in so-called cumulative assessment groups (CAG) which are primarily based on their toxicodynamic properties, that is, respective target organs and mode of action (MoA). In this context, compounds causing toxicity by a similar MoA are assumed to follow a model of dose/concentration addition (DACA). However, the respective approach inherently falls short of addressing cases where there are dissimilar or independent MoAs resulting in wider toxicokinetic effects. Yet, the latter are often the underlying cause when effects deviate from the DACA model. In the present manuscript, we therefore suggest additionally to consider toxicokinetic effects (especially related to xenobiotic metabolism and transporter interaction) for the grouping of substances to predict mixture toxicity. In line with the concept of MoA-based CAGs, we propose common kinetics groups (CKGs) as an additional tool for grouping of chemicals and mixture prioritization. Fundamentals of the CKG concept are discussed, along with challenges for its implementation, and methodological approaches and examples are explored.

A targeted transcriptomics approach for the determination of mixture effects of pesticides

While real-life exposure occurs to complex chemical mixtures, toxicological risk assessment mostly focuses on individual compounds. There is an increasing demand for in vitro tools and strategies for mixture toxicity analysis. Based on a previously established set of hepatotoxicity marker genes, we analyzed mixture effects of non-cytotoxic concentrations of different pesticides in exposure-relevant binary mixtures in human HepaRG hepatocarcinoma cells using targeted transcriptomics. An approach for mixture analysis at the level of a complex endpoint such as a transcript pattern is presented, including mixture design based on relative transcriptomic potencies and similarities. From a mechanistic point of view, goal of the study was to evaluate combinations of chemicals with varying degrees of similarity in order to determine whether differences in mechanisms of action lead to different mixtures effects. Using a model deviation ratio-based approach for assessing mixture effects, it was revealed that most data points are consistent with the assumption of dose addition. A tendency for synergistic effects was only observed at high concentrations of some combinations of the test compounds azoxystrobin, cyproconazole, difenoconazole, propiconazole and thiacloprid, which may not be representative of human real-life exposure. In summary, the findings of our study suggest that, for the pesticide mixtures investigated, risk assessment based on the general assumption of dose addition can be considered sufficiently protective for consumers. The way of data analysis presented in this paper can pave the way for a more comprehensive use of multi-gene expression data in experimental studies related to mixture toxicity.