Structure–activity relationships for hepatocyte toxicity and electrophilic reactivity of <i>α</i>,<i>β</i>‐unsaturated esters, acrylates and methacrylates
Bibliographic record
Abstract
Covalent binding of reactive electrophiles to cellular targets is a molecular interaction that has the potential to initiate severe adverse biological effects. Therefore, electrophile reactivity towards biological nucleophiles could serve as an important correlate for toxic effects such as hepatocyte death. To determine if reactivity correlates with rat hepatotoxicity, alpha,beta-unsaturated esters, consisting of acrylates and methacrylates, that are inherently electrophilic and exhibit widely varying degrees of reactivity were investigated. Reactivity was measured using simple assays with glutathione and butylamine as surrogates for soft thiol and hard amino biological nucleophile targets. A linear relationship was observed between hepatotoxicity and thiol reactivity only, while no amine reactivity was observed. Structure-activity relationships were also investigated, with results showing toxicity was well modeled by electronic parameters E(LUMO) and partial charge of the carbon atoms in the reactive center. No relationship was observed between toxicity and logP. These results suggest that differences in hepatocyte toxicity of acrylates and methacrylates can be related to their electrophilic reactivity which corresponds to their ability to deplete GSH and protein thiols.
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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 | 0.000 | 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".