Enhancing the Bioavailability of Resveratrol: Combine It, Derivatize It, or Encapsulate It?
Bibliographic record
Abstract
Overcoming the limited bioavailability and extensive metabolism of effective in vitro drugs remains a challenge that limits the translation of promising drugs into clinical trials. Resveratrol, despite its well-reported therapeutic benefits, is not metabolically stable and thus has not been utilized as an effective clinical drug. This is because it needs to be consumed in large amounts to overcome the burdens of bioavailability and conversion into less effective metabolites. Herein, we summarize the more relevant approaches to modify resveratrol, aiming to increase its biological and therapeutic efficacy. We discuss combination therapies, derivatization, and the use of resveratrol nanoparticles. Interestingly, the combination of resveratrol with established chemotherapeutic drugs has shown promising therapeutic effects on colon cancer (with oxaliplatin), liver cancer (with cisplatin, 5-FU), and gastric cancer (with doxorubicin). On the other hand, derivatizing resveratrol, including hydroxylation, amination, amidation, imidation, methoxylation, prenylation, halogenation, glycosylation, and oligomerization, differentially modifies its bioavailability and could be used for preferential therapeutic outcomes. Moreover, the encapsulation of resveratrol allows its trapping within different forms of shells for targeted therapy. Depending on the nanoparticle used, it can enhance its solubility and absorption, increasing its bioavailability and efficacy. These include polymers, metals, solid lipids, and other nanoparticles that have shown promising preclinical results, adding more "hype" to the research on resveratrol. This review provides a platform to compare the different approaches to allow directed research into better treatment options with resveratrol.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".