Vitamins as excipients in pharmaceutical products
Bibliographic record
Abstract
Excipients are ingredients in pharmaceutical products other than the active ingredient, added to facilitate manufacturing, enhance stability or modulate release and bioavailability. Vitamins are diverse molecules essential for human nutrition that also can fulfil excipient functions. This review focuses on vitamins used as excipients and provides an overview of the functions of vitamins in various pharmaceutical formulations. A thorough search was conducted to understand the current use of vitamins in marketed drug products, concluding that many vitamins are already used as functional excipients. Vitamins are used widely in different dosage forms, including oral, parenteral, and topical formulations, and alongside a broad range of active pharmaceutical ingredients, biologics, and small molecules from different biopharmaceutical classification system classes. Many examples of the use of vitamins to improve the performance of the pharmaceutical formulation in which they are included are presented and the mode of action of vitamins as excipients in the product is reviewed. Furthermore, the potential for future uses of vitamins in pharmaceutical products is highlighted. Lastly, considerations for the use of vitamins as excipients in drug products as well as the regulatory framework are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".