Toxicity of Haloacetonitrile Mixtures to a Normal Tissue-Derived Human Cell Line: Are They Additive, Synergistic, or Antagonistic?
Bibliographic record
Abstract
Haloacetonitriles (HANs)─a class of nitrogen-containing disinfection byproducts found in treated drinking water─are cytotoxic and genotoxic to mammalian cells. However, most cell toxicity data have been ascertained by using transformed animal- or cancer-derived human cell lines. In this study, we evaluated the cytotoxicity of individual chloro-, bromo-, and iodo-acetonitrile (ClCH 2 CN, BrCH 2 CN, and ICH 2 CN) and their mixtures using normal tissue-derived human epithelium-derived RPE-1 hTERT cells. The order for individual HAN cytotoxicity from most to least toxic was ICH 2 CN > BrCH 2 CN ≫ ClCH 2 CN with the inhibitory concentration that reduced the cell viability by 50% of the untreated cells (IC 50 ) of 2.52 ± 0.19, 7.24 ± 0.68, and 190 ± 18.5 μM, respectively. For HAN mixtures, cytotoxicity from most to least toxic was BrCH 2 CN+ICH 2 CN > ICH 2 CN+ClCH 2 CN ≈ ClCH 2 CN+BrCH 2 CN+ICH 2 CN > ClCH 2 CN+BrCH 2 CN with a total IC 50 of 4.65 ± 0.71, 8.12 ± 1, 7.91 ± 0.64, and 13.6 ± 2.04 μM, respectively. The cytotoxicity of all four mixtures at IC 50 was well predicted by both concentration addition (CA) and independent action (IA) models, which confirmed additivity effects. However, the Chou–Talalay method (CT) showed antagonistic cytotoxic effects. The difference could primarily stem from the different threshold criteria of each model for additivity, synergy, and antagonism, leading to different conclusions. Results indicate that evaluating cumulative mixture toxic effects with CA, IA, and CT can improve the overall confidence of the analysis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".