In Vivo Effects of Naproxen, Salicylic Acid, and Valproic Acid on the Pharmacokinetics of Trichloroethylene and Metabolites in Rats
Bibliographic record
Abstract
It was recently demonstrated that some drugs modulate in vitro metabolism of trichloroethylene (TCE) in humans and rats. The objective was to assess in vivo interactions between TCE and three drugs: naproxen (NA), valproic acid (VA), and salicylic acid (SA). Animals were exposed to TCE by inhalation (50 ppm for 6 h) and administered a bolus dose of drug by gavage, equivalent to 10-fold greater than the recommended daily dose. Samples of blood, urine, and collected tissues were analyzed by headspace gas chromatography coupled to an electron capture detector for TCE and metabolites (trichloroethanol [TCOH] and trichloroacetate [TCA]) levels. Coexposure to NA and TCE significantly increased (up to 50%) total and free TCOH (TCOHtotal and TCOHfree, respectively) in blood. This modulation may be explained by an inhibition of glucuronidation. VA significantly elevated TCE levels in blood (up to 50%) with a marked effect on TCOHtotal excretion in urine but not in blood. In contrast, SA produced an increase in TCOHtotal levels in blood at 30, 60, and 90 min and urine after coexposure. Data confirm in vitro observations that NA, VA, and SA affect in vivo TCE kinetics. Future efforts need to be directed to evaluate whether populations chronically medicated with the considered drugs display greater health risks related to TCE exposure.
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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 | 0.001 | 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".