An ultrafast insulin formulation enabled by high-throughput screening of engineered polymeric excipients
Bibliographic record
Abstract
Insulin has been used to treat diabetes for almost 100 years; yet, current rapid-acting insulin formulations do not have sufficiently fast pharmacokinetics to maintain tight glycemic control at mealtimes. Dissociation of the insulin hexamer, the primary association state of insulin in rapid-acting formulations, is the rate-limiting step that leads to delayed onset and extended duration of action. A formulation of insulin monomers would more closely mimic endogenous postprandial insulin secretion, but monomeric insulin is unstable in solution using present formulation strategies and rapidly aggregates into amyloid fibrils. Here, we implement high-throughput-controlled radical polymerization techniques to generate a large library of acrylamide carrier/dopant copolymer (AC/DC) excipients designed to reduce insulin aggregation. Our top-performing AC/DC excipient candidate enabled the development of an ultrafast-absorbing insulin lispro (UFAL) formulation, which remains stable under stressed aging conditions for 25 ± 1 hours compared to 5 ± 2 hours for commercial fast-acting insulin lispro formulations (Humalog). In a porcine model of insulin-deficient diabetes, UFAL exhibited peak action at 9 ± 4 min, whereas commercial Humalog exhibited peak action at 25 ± 10 min. These ultrafast kinetics make UFAL a promising candidate for improving glucose control and reducing burden for patients with 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".