Herbicides and pesticides synergistically interact at low concentrations in complex mixtures
Bibliographic record
Abstract
Assessing a complex mixture of pesticides at the impacted sites has been challenging for risk assessors for 50 years. The default assumption is that at low concentrations, pesticides interact additively with one another; thus, the risk posed by each component of a complex mixture could be simply added up. The EPA interaction-based hazard index (HIInteraction) modifies this assumption using a binary weight-of-evidence (BINWOE). However, data gaps often preclude HIInteraction use at most sites. This study evaluated these assumptions using the BINWOE to estimate the hazard index (HI) of select pesticide mixtures. The lack of in vivo binary interaction data led us to use a cell line, SH-SY5Y, to obtain the data necessary for the BINWOE approach. In the risk assessment, we considered the most active exposure scenario inhaling a mixture of volatile pesticides from contaminated soil and groundwater. The potential interactions between pesticides in 15 binary mixtures were investigated using the MTT assay in SH-SY5Y cells. Our findings showed that 60% of the binary mixtures elicited synergism (in at least one concentration), 27% displayed antagonism, and 13% showed additive effects in SH-SY5Y cells. Combining human safety data with in vitro interaction data indicated that adults and toddlers were at the highest risk when considering industrial and commercial land use, respectively, compared to other subpopulations. Incorporating interaction data into the risk assessment either increased the risk by up to 20% or decreased the risk by 2%, depending on the mixture. Our results demonstrate the predominant synergistic interactions, even at low concentrations, altered risk characterization at the complex operating site. Most concerning, organochlorine pesticides with the same mechanism of action did not follow dose additivity when evaluated by SH-SY5Y cell lines. Based on our observations, we caution that current HI methods based on additivity assumptions may underestimate the risk of organochlorine mixtures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".