Effects of Age and Polytherapy, Risk Factors of Valproic Acid (VPA) Hepatotoxicity, on the Excretion of Thiol Conjugates of (<i>E</i>)‐2,4‐diene VPA in People with Epilepsy Taking VPA
Bibliographic record
Abstract
PURPOSE: Valproic acid (VPA) is an antiepileptic drug (AED) used for generalized and absence seizures. It has a rare but potentially fatal hepatotoxicity side effect, and many researchers believe that reactive metabolites of VPA could be involved. We demonstrated here that the thiol conjugates of (E)-2,4-diene VPA were significantly elevated in a high-risk group of patients. METHODS: Thirty-four patients with seizures were divided into three groups. Group A (n = 14) were being treated with VPA; group B (n = 12) received VPA as well as other AEDs that do not induce P450-VPA metabolism; and group C (n = 8) received VPA and AEDs that induce P450-VPA metabolism. The NAC conjugates of (E)-2,4-diene VPA (NAC I and NAC II) were identified in the urine of patients by gas chromatography/mass spectrography NICI analysis. RESULTS: VPA monotherapy (group A) or VPA polytherapy with non-P450-enzyme-inducing drugs (group B), showed that patients younger than 7.5 years excreted significantly higher concentrations of the two conjugates compared with older patients (older than 7.5 years) in the same groups (p < 0.05). Patients receiving VPA polytherapy with P450-enzyme-inducing drugs were all older than 7. 5 years (group C). They excreted significantly higher concentrations of NAC I and NAC II compared with patients in groups A and B who were older than 7.5 years (p < 0.05). CONCLUSIONS: There were no significant differences in the excretion of NAC I and NAC II between patients in group C and those who were 7.5 years or younger in groups A and B. High doses of VPA also were a significant factor associated with elevated NAC I and NAC II among young patients and in polytherapy patients.
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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) | 0.001 | 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".