Structure–activity relationships for thiol reactivity and rat or human hepatocyte toxicity induced by substituted <i>p</i>‐benzoquinone compounds
Bibliographic record
Abstract
Covalent binding of toxic chemicals to cellular targets is a molecular interaction that initiates a wide array of adverse biological effects. The creation of a covalent bond can be cited as a key initiating step along many toxicity pathways which must be predicted in order to predict the potential of a chemical to cause specific harmful effects. Currently, quantitative structure-activity relationship (QSAR) models are being improved by focusing on endpoints such as simple electrophile reactivity for covalent interactions rather than on commonly used complex toxicity endpoints. The cytotoxicity and electrophilic reactivity of 10 p-substituted benzoquinone derivatives, which are well known electrophilic alkylating agents, were investigated under the premise that QSAR toxicity models can be improved when the molecular triggering event is considered. Hepatocyte toxicity was determined by incubation of individual compounds with freshly isolated rat or cryopreserved human hepatocyte suspensions. The potential for chemical reactivity between a chemical and cellular target was measured by determining non-enzymic reactivity with glutathione, representing thiol nucleophiles. The decline in free thiol moieties was measured to characterize the electrophile reactivity. It was found that the degree of rat hepatotoxicity induced by benzoquinones correlated with the rate at which they non-enzymically react with glutathione and to various global and atomic electronic frontier orbital parameters which described electrophilicity. Human hepatocytes showed similar results but the statistical significance was much lower. The QSAR expressions suggest that covalent binding reactivity serves as a good correlate to hepatotoxicity and could improve QSAR modeling for potential toxicity risks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".