The stability of insulin solutions in syringes is improved by ensuring lower molecular weight silicone lubricants are absent
Bibliographic record
Abstract
Protein drugs such as insulin are almost universally delivered via glass syringes lubricated with silicone oil. It is not uncommon for prefilled syringes (PFS) to become cloudy, which may affect bioavailability or total drug dose. To examine the role, if any, of the silicone oil lubricant in this process, a systematic evaluation of the degree of insulin denaturation and aggregation as a function of silicone oils of different molecular weights was undertaken. The former was measured using fluorescence changes of aqueous insulin/silicone dispersions, while the latter examined changes in turbidity as a function of mixing and silicone oil type; the results were confirmed at two different insulin concentrations and agitation speeds. Lower molecular weight silicones led to the most rapid denaturation and aggregation, and when examined in blends of silicones at a fixed viscosity of 1000 cSt, commonly used for syringe lubrication, more rapid denaturation/aggregation was noted in blends of silicones containing the largest fractions of low molecular weight materials. As a consequence, the molecular weight profile of silicone lubricants should be established prior to the preparation of prefilled syringes.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".