Encapsulation of a Small Hydrophilic Drug in Injectable PLGA Microparticles for Treatment of Pancreatic Cancer
Bibliographic record
Abstract
Pancreatic cancer is the fourth largest cause of cancer-related deaths in Canada, and has the highest mortality rate of all major cancers. The typical methods of treatment: chemotherapy and radiation have significant side effects, as they do not target the tumour specifically. Targeted therapies are being developed that would specifically affect the pancreatic tumour, leaving healthy cells undamaged. A small, hydrophilic drug has been shown to inhibit the activity of Neu1 sialidase, an enzyme involved in the activation of the growth process, which is upregulated in tumour cells. Previous research has shown that encapsulation of the drug in a surgically implanted PLGA capsule allows for drug release over a period of several weeks, inhibiting tumour growth. A microparticle form is desired, to decrease the invasiveness of the treatment. Encapsulation of the drug was performed using an aqueous double-emulsion method, resulting in a drug encapsulation efficiency of 37%. A mean particle size of 125 was obtained, within the range acceptable for injectable particles. Further experiments will be performed to compare the encapsulation efficiency with microparticles prepared using an organic single-emulsion method. The release kinetics of the drug will be characterized in vitro using HPLC analysis, and its effectiveness in inhibiting tumour growth will be assessed using tumour cell cultures and animal models. Should this dosage form prove effective at inhibiting tumour growth, it may lead to the formulation of an injectable dosage form capable of sustained release.
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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".