Human and ecological risk assessment of a crop protection chemical: a case study with the azole fungicide epoxiconazole
Bibliographic record
Abstract
Conventional risk assessments for crop protection chemicals compare the potential for causing toxicity (hazard identification) to anticipated exposure. New regulatory approaches have been proposed that would exclude exposure assessment and just focus on hazard identification based on endocrine disruption. This review comprises a critical analysis of hazard, focusing on the relative sensitivity of endocrine and non-endocrine endpoints, using a class of crop protection chemicals, the azole fungicides. These were selected because they are widely used on important crops (e.g. grains) and thereby can contact target and non-target plants and enter the food chain of humans and wildlife. Inhibition of lanosterol 14α-demethylase (CYP51) mediates the antifungal effect. Inhibition of other CYPs, such as aromatase (CYP19), can lead to numerous toxicological effects, which are also evident from high dose human exposures to therapeutic azoles. Because of its widespread use and substantial database, epoxiconazole was selected as a representative azole fungicide. Our critical analysis concluded that anticipated human exposure to epoxiconazole would yield a margin of safety of at least three orders of magnitude for reproductive effects observed in laboratory rodent studies that are postulated to be endocrine-driven (i.e. fetal resorptions). The most sensitive ecological species is the aquatic plant Lemna (duckweed), for which the margin of safety is less protective than for human health. For humans and wildlife, endocrine disruption is not the most sensitive endpoint. It is concluded that conventional risk assessment, considering anticipated exposure levels, will be protective of both human and ecological health. Although the toxic mechanisms of other azole compounds may be similar, large differences in potency will require a case-by-case risk assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".