Reduction of enantioselectivity in the kinetic disposition and metabolism of verapamil in rats exposed to toluene
Bibliographic record
Abstract
Toluene and verapamil are subject to extensive oxidative metabolism mediated by CYP enzymes, and their interaction can be stereoselective. In the present study we investigated the influence of toluene inhalation on the enantioselective kinetic disposition of verapamil and its metabolite, norverapamil, in rats. Male Wistar rats (n = 6 per group) received a single dose of racemic verapamil (10 mg/kg) orally at the fifth day of nose-only toluene or air (control group) inhalation for 6 h/day (25, 50, and 100 ppm). Serial blood samples were collected from the tail up to 6 h after verapamil administration. The plasma concentrations of verapamil and norverapamil enantiomers were analyzed by LC-MS/MS by using a Chiralpak AD column. Toluene inhalation did not influence the kinetic disposition of verapamil or norverapamil enantiomers (p > 0.05, Kruskal-Wallis test) in rats. The pharmacokinetics of verapamil was enantioselective in the control group, with a higher plasma proportion of the S-verapamil (AUC 250.8 versus 120.4 ng x h x mL(-1); p < or = 0.05, Wilcoxon test) and S-norverapamil (AUC 72.3 versus 52.3 ng x h x mL(-1); p < or = 0.05, Wilcoxon test). Nose-only exposure to toluene at 25, 50, or 100 ppm resulted in a lack of enantioselectivity for both verapamil and norverapamil. The study demonstrates the importance of the application of enantioselective methods in studies on the interaction between solvents and chiral drugs.
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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) | 0.000 | 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 | 0.001 |
| 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".