Catechol-based chemistry for hypoglycemia-responsive delivery of zinc-glucagon via hydrogel-based microneedle patch technology
Bibliographic record
Abstract
Hypoglycemia is a serious and potentially life-threatening condition for people with insulin-dependent diabetes. To provide a safeguard against hypoglycemia, we introduce a “smart” microneedle (MN) patch that senses glucose levels and delivers a blood glucose-raising agent (Zinc-Glucagon (Z-GCN)) in response to hypoglycemia. Herein, we describe the use of catechol and boronic acid chemistry to design a self-crosslinkable hydrogel-based MN that stimulates the release of Z-GCN during hypoglycemia. In this design, the catechol groups bind to Z-GCN through metal-ligand complexation. At hyperglycemia, boronic acids react with glucose to generate cyclic boronate esters. As the glucose concentration decreases, the boronic acid groups dissociate and are favored over Z-GCN in binding with catechol, which promotes the release of Z-GCN. We fully characterize the fabricated MN in vitro. Moreover, we further evaluate the MN and demonstrate the in vivo glucose-responsive delivery of Z-GCN from the patch. We also show its effectiveness in preventing hypoglycemia for up to 6 h in type 1 diabetic male rats against two consecutive insulin overdose challenges. Since many proteins/peptides have a high binding affinity to metal ions, the introduced mechanism driven by the competitive binding of catechol-metal ions has great implications in drug delivery applications of various protein/peptide-based therapeutics. Hypoglycemia is a serious and potentially life-threatening condition for people with insulin dependent diabetes, but preventative hypoglycemia therapies are elusive. Here, the authors report the use of catechol and boronic acid chemistry to design a self-crosslinkable hydrogel-based microneedle patch that delivers Zinc-Glucagon at low glucose levels and prevents insulin-induced hypoglycemia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".