Unlocking The Potential of Aceclofenac: Investigating Strategies To Enhance Oral Bioavailability and Therapeutic Efficacy
Bibliographic record
Abstract
Oral bioavailability of aceclofenac is very low (only 14%) due to instability and incomplete intestinal absorption and extensive gut wall extraction. Aceclofenac, a potent NSAID, faces challenges related to its limited bioavailability, hampering its therapeutic efficacy. This study investigates the potential of naringin, a natural flavonoid known for its bioenhancement properties, to improve aceclofenac's bioavailability. Formulations of aceclofenac containing naringin were developed and characterized for physicochemical properties. In vitro dissolution studies revealed enhanced drug release rates compared to control formulations. Pharmacokinetic studies in animal models demonstrated significantly improved oral bioavailability of aceclofenac when co-administered with naringin, attributed to increased intestinal absorption and enhanced drug solubility. These findings highlight the promising role of naringin in augmenting the bioavailability of aceclofenac formulations. This approach holds potential for developing more effective oral dosage forms of aceclofenac, offering enhanced therapeutic outcomes for patients managing pain and inflammation. Further optimization of formulation parameters and clinical investigations are warranted to validate the clinical efficacy and safety of naringin-enhanced aceclofenac formulations.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".