Development of a Topical Resveratrol Formulation for Commercial Applications Using Dendrimer Nanotechnology
Bibliographic record
Abstract
Resveratrol (RSV) is well known for its anti-oxidant and anti-aging properties. However, resveratrol is insoluble in water and has stability issues. Recently, efforts were placed to prepare a resveratrol-based advanced anti-aging topical product but it contains harsh organic solvents and oils that could be harmful to the human body and the environment. Hence, we propose the use of a multifunctional dendrimer to solve the solubility and stability issues of resveratrol. A dendrimer-resveratrol complex was prepared, optimized and tested for solubility enhancement, stability in solution and cream dosage forms. We have also developed a high performance liquid chromatography method to measure the resveratrol within the final product. PAMAM dendrimers increased the solubility and stability of resveratrol in water and semisolid dosage forms. Therefore, this product would be water based 'green' formulation devoid of harsh organic solvents and oils and can be safely applied to the skin. Additionally, we have shown that the dendrimer helped to increase overall RSV loading and skin penetration of resveratrol. The dendrimer-RSV formulation was successfully scaled up towards commercialization. Dendrimer with RSV has led to an innovation in anti-aging cream and solutions that could be commercially marketed. Dendrimer-RSV complex could also be added to other product forms for additional purposes and applications.
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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 | 0.001 |
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".