Evaluating In Situ (E)‐2,4‐Diene‐Valproic Acid in the Toxicity of Valproic Acid and (E)‐2‐Ene‐Valproic Acid in Sandwich‐Cultured Rat Hepatocytes
Bibliographic record
Abstract
Formation of reactive metabolites like ( E )‐2,4‐diene‐VPA is a proposed mechanism for the idiosyncratic hepatotoxicity of valproic acid (VPA). ( E )‐2,4‐diene‐VPA is formed by cytochrome P450 (CYP)‐mediated desaturation of ( E )‐2‐ene‐VPA or mitochondrial β‐oxidation of 4‐ene‐VPA, which itself is a CYP‐catalyzed metabolite of VPA. ( E )‐2,4‐diene‐VPA is reactive and more hepatotoxic than VPA, but direct experimental evidence is needed to evaluate the effect of the in situ generated metabolite on VPA hepatotoxicity. We assessed the effect of modulating the in situ formation of ( E )‐2,4‐diene‐VPA by pretreatment with phenobarbital (PB, a CYP inducer) and 1‐aminobenzotriazole (1‐ABT, a CYP inhibitor) on markers of oxidative stress, steatosis and necrosis in sandwich‐cultured rat hepatocytes treated with VPA or ( E )‐2‐ene‐VPA. PB increased the metabolism of ( E )‐2‐ene‐VPA to ( E )‐2,4‐diene‐VPA, and this was accompanied by enhanced toxicity of ( E )‐2‐ene‐VPA, whereas 1‐ABT attenuated the increase in the levels of the ( E )‐2,4‐diene‐VPA metabolite and toxicity by PB. Neither PB nor 1‐ABT affected ( E )‐2,4‐diene‐VPA formation and toxicity in VPA‐treated hepatocytes. In conclusion, in situ formed ( E )‐2,4‐diene‐VPA contributes to the toxicity in sandwich‐cultured rat hepatocytes, if generated at sufficiently high levels. [Supported by CIHR and MSFHR]
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".