Application of structure–activity relationships to investigate the molecular mechanisms of hepatocyte toxicity and electrophilic reactivity of <i>α</i>,<i>β</i>‐unsaturated aldehydes
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, a measure for electrophilic reactivity with biological nucleophiles could serve as an important correlate to toxic effects such as hepatocyte death. To determine if electrophile reactivity correlates with rat hepatocyte cytotoxicity, the inherently electrophilic alpha,beta-unsaturated aldehydes were chosen for investigation. Reactivity was measured with simple assays that used glutathione, a soft nucleophile, and butylamine, a harder nucleophile, as models for protein thiol and amine nucleophilic sites, respectively. Despite their higher reactivity with thiols, a linear relationship was only observed between hepatocyte cytotoxicity and amine reactivity. Structure-activity relationships were also investigated for hepatocyte toxicity, and results showed toxicity was well modelled by log P and electronic parameters E(LUMO) and partial charge of the carbonyl carbon (C'(carb)). Hydrophobicity and electronic descriptors were only significant in separate distinct models, suggesting that there were simultaneously occurring mechanisms that affected toxicity. Log P was linked to the ease of oxidation by a microsomal aldehyde dehydrogenase enzyme, while the electronic descriptors and amine reactivity were linked to direct alkylation. Even with the presence of electrophile characteristics, alpha,beta-unsaturated aldehyde hepatocyte toxicity could not be predicted exclusively by electrophilic reactivity as oxidative metabolism was also a factor for toxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".