Influence of permeability enhancers on the paracellular permeability of metformin hydrochloride and furosemide across Caco-2 cells
Bibliographic record
Abstract
Permeability enhancers can affect absorption of paracellularly transported drugs. This study aims to evaluate effects of permeability enhancers (chitosan, methyl- β -cyclodextrin, sodium caprate, sodium lauryl sulfate, etc.) on the permeability of paracellularly absorbed furosemide and metformin hydrochloride. Methyl thiazole tetrazolium bromide test was carried out to determine the drug concentrations in permeability study. Trans-epithelial electrical resistance (TEER) values determined to assess the integrity of tight junctions. Permeability enhancers were applied at different concentrations alone, in dual/triple combinations. Permeability was determined using human colorectal adenocarcinoma (Caco-2) cells (TEER > 400 Ω·cm2). Permeability enhancers have no significant effect (<2-fold; p > 0.05) on the permeability of furosemide (1.80 × 10−5 ± 4.55 × 10−7 cm/s); however, metformin permeability (1.36 × 10−5 ± 1.25 × 10−6 cm/s) increased significantly ( p < 0.05) with 0.3% and 0.5% ( w/v) chitosan (2.0- and 2.7-fold, respectively), 1% methyl- β -cyclodextrin ( w/v) (3.5-fold), 10 and 20 µmol/L sodium caprate (2.2- and 2.8-fold, respectively), and 0.012% sodium lauryl sulfate ( w/v) (1.9-fold). Furosemide permeability increased significantly ( p < 0.05) with chitosan-sodium lauryl sulfate combination (1.7-fold), and all triple combinations (1.4- to 1.9-fold). Chitosan containing dual/triple combinations resulted in significant increase ( p < 0.05) in metformin permeability (1.7 to 2.8-fold). All results indicated that absorption of furosemide and metformin can be improved by the combination of permeability enhancers. Therefore, it can be evaluated for the formulation of development strategies containing furosemide and metformin by the pharmaceutical industry.
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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 | 0.000 |
| 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".