The effects of colesevelam HCl on the single‐dose pharmacokinetics of glimepiride, extended‐release glipizide, and olmesartan medoxomil
Bibliographic record
Abstract
Bile acid sequestrants can potentially bind to concomitant drugs. Single-dose studies evaluated the effects of colesevelam on the pharmacokinetics of glimepiride, glipizide extended-release (ER), and olmesartan medoxomil. Each study enrolled healthy subjects aged 18-45 years. The olmesartan medoxomil study used a randomized adaptive crossover design that initially compared olmesartan medoxomil alone versus simultaneously with colesevelam, then olmesartan medoxomil alone versus 4 hours before colesevelam. The other studies used a three-period crossover design (test drug alone, test drug simultaneously with colesevelam, and test drug 4 hours before colesevelam). For the colesevelam coadministration periods, 3,750 mg once daily was dosed throughout the pharmacokinetic sampling period. After each single dose of test drug, serial blood samples were collected for determination of plasma drug concentrations and calculation of pharmacokinetic parameters. Administering colesevelam simultaneously with glimepiride or glipizide ER resulted in minor reductions (18% and 13%, respectively) in total exposure that were negated by staggering colesevelam dosing by 4 hours. Administering colesevelam simultaneously with olmesartan medoxomil resulted in a major reduction (39%) in olmesartan exposure that was reduced by staggering colesevelam dosing by 4 hours. This reduction in olmesartan exposure is not predicted to have a clinically significant impact on blood pressure control.
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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) | 0.001 | 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".