Buoyancy-Generating Agents for Stomach-Specific Drug Delivery: An Overview with Special Emphasis on Floating Behavior
Bibliographic record
Abstract
Gastric retentive drug delivery provides a promising technology exhibiting an extended gastric residence and a drug release independent of patient related variables. It is usually useful in improving local gastric treatment as well as overcoming drug-related problems .i.e. drugs having narrow absorption window, short half-life or low intestinal solubility. Buoyancy is considered one of the most promising approaches for gastro-retention of dosage forms. Floating drug delivery systems have a bulk density lower than gastric fluids and thus remain buoyant in the stomach causing an increase in gastric residence time. The buoyancy of these systems is attained by the aid of substances responsible to generate the low density. Various agents with different mechanisms were adopted either gas-generating agents, air entrapping swellable polymers, inherent low density substances, porous excipients, hollow/porous particles inducing preparation techniques or sublimating agents. Therefore, this review gives an exclusive descriptive classification of the different categories of these buoyancy-generating agents while representing the related research works. An overview is also conducted to describe relevant techniques assessing the floating behavior of such dosage forms either in vitro or in vivo. Finally, a collection representing FDA-approved floating pharmaceutical products is adopted with emphasis on the buoyancy-generating agent type used in each product.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".