In vitro controlled drug release from loaded microspheres - dose regulation through formulation
Bibliographic record
Abstract
PURPOSE: Drug release profiles were established for ibuprofen encapsulated within several types of microspheres and a range of dissolution buffer media to study the effect these variables have in controlling the rate and extent of drug release. METHODS: Fatty acid microspheres containing ibuprofen were prepared by a process previously developed and refined to produce microspheres of a known size and composition, namely 80-125 mum diameter and an excipient to ibuprofen ratio of 3:1. Drug release profiles from these encapsulated formulations were compared with those obtained for the dissolution of ibuprofen alone under the same conditions. RESULTS: Stearic acid microspheres were found to only partially retard the release of ibuprofen over a twenty minute period compared with the dissolution of ibuprofen alone. However, a significant retardation of ibuprofen release was observed with cetostearyl alcohol microspheres over the same period of time. Secondly, drug release profiles for encapsulated ibuprofen were determined using five distinct dissolution buffer media; sodium phosphate, potassium phosphate, citric acid and phosphate mix, MOPS and tris. Significant differences in the extent and rate of drug release were recorded between the different dissolution buffer solutions. These differences were also shown to be independent of variations in pH, temperature, buffer concentrations and the type of cations present. CONCLUSIONS: The presence and choice of microsphere formulation, and the choice of buffer present in the dissolution solution, can influence drug release in vitro, i.e. it is possible to achieve controlled drug release from microspheres. To explain the control achieved through the choice of buffer in solution it is proposed that the buffer anion exerts a stabilising influence on the ibuprofen-microsphere matrix.
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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 | 0.000 |
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".