Application of Quantitative Structure-Toxicity Relationships for the Comparison of the Cytotoxicity of 14 p-Benzoquinone Congeners in Primary Cultured Rat Hepatocytes Versus PC12 Cells
Bibliographic record
Abstract
Quinones are believed to induce their toxicity by two main mechanisms: oxygen activation by redox cycling and alkylation of essential macromolecules. The physicochemical parameters that underlie this activity have not been elucidated, although redox potential is believed to play a significant role. In this study, we have evaluated the cytotoxicity, formation of reactive oxygen species (ROS), and the glutathione (GSH) depleting ability of 14 p-benzoquinone congeners in primary rat hepatocyte and PC12 cell cultures. All experiments were performed under identical conditions (37 degrees C, 5% CO2/air) in 96-well plates. The most cytotoxic quinone was found to be tetrachloro-p-benzoquinone (chloranil), and the least toxic was duroquinone or 2,6-di-tert-butyl-p-benzoquinone. The cytotoxic order varied between the cell types, and in particular, the di-substituted methoxy or methyl p-benzoquinones were particularly more cytotoxic towards PC12 cells. We have derived one- and two-parameter quantitative structure-toxicity relationships (QSTRs) which revealed that the most cytotoxic quinones had the highest electron affinity and the smallest volume. Cytotoxicity did not correlate with the lipophilicity of the quinone. Furthermore, we found that p-benzoquinone cytotoxicity correlated well with hepatocyte ROS formation and GSH depletion, whereas in PC12 cells, cytotoxicity did not correlate with ROS formation and somewhat correlated with GSH depletion. Hepatocytes had far greater hydrogen peroxide detoxifying capacity than PC12 cells, but PC12 cells contained more GSH/mg protein. Thus, p-benzoquinone-induced ROS formation was greater towards PC12 cells than with hepatocytes. To our knowledge, this is the first QSTR derived for p-benzoquinone cytotoxicity in these cell types and could form the basis for distinguishing certain cell-specific cytotoxic mechanisms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".