Leveraging toxicogenomics in a weight of evidence approach to demonstrate a CAR-mediated mode of action for cyclobutrifluram-related mouse liver tumors
Bibliographic record
Abstract
Toxicogenomics-based approaches are powerful tools for investigating the mode of action and human relevance of chemical-induced effects in animal toxicity studies, thus supporting human risk assessment and regulatory decisions. Here, we incorporated transcriptomics and metabolomics into a mode of action assessment of male mouse liver tumors observed following 80-week dietary exposure to cyclobutrifluram, a novel complex II succinate dehydrogenase inhibitor agrochemical. The assessment was conducted using the framework developed by the International Programme on Chemical Safety (IPCS) and the International Life Sciences Institute (ILSI), based on activation of the nuclear constitutive androstane receptor (CAR) and subsequent downstream events that have been established as human non-relevant. Cyclobutrifluram was shown to activate rat, mouse, and human CAR in in vitro transactivation assays. Dietary administration of cyclobutrifluram in male mice was associated with time- and/or dose-dependent liver weight increases, centrilobular hepatocellular hypertrophy, induction of CAR-related liver enzyme activity, specifically CYP2B and CYP3A, and hepatocellular proliferation. Transcriptomics analysis of mouse liver identified cyclobutrifluram-induced gene expression profiles consistent with CAR activation, based on published signatures and similarity to the reference CAR inducer phenobarbital. Metabolomics analysis of mouse plasma and liver further indicated that cyclobutrifluram induced similar biochemical changes as phenobarbital, with no evidence of any additional activity. Overall, this work demonstrates how toxicogenomics can provide valuable weight of evidence to identify the mode of action for chemical-induced rodent liver tumors and to exclude alternative modes of action.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".