Engineering Insulin Cold Chain Resilience to Improve Global Access
Bibliographic record
Abstract
ABSTRACT There are 150 million people with diabetes worldwide who require insulin replacement therapy and the prevalence of diabetes is rising fastest in middle and low-income countries. Current formulations require costly refrigerated transport and storage to prevent loss of insulin integrity. This study shows the development of simple “drop-in” amphiphilic copolymer excipients to maintain formulation integrity, bioactivity, pharmacokinetics and pharmacodynamics for over 6 months when subjected to severe stressed aging conditions that cause current commercial formulation to fail in under 2 weeks. Further, when these copolymers are added to Humulin R (Eli Lilly) in original commercial packaging they prevent insulin aggregation for up to 4 days at 50 °C compared to less than 1 day for Humulin R alone. These copolymers demonstrate promise as simple formulation additives to increase the cold chain resilience of commercial insulin formulations, thereby expanding global access to these critical drugs for treatment of diabetes.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".