Sustained Delivery of GLP-1 Receptor Agonists from Injectable Biomimetic Hydrogels Improves Treatment of Diabetes
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) is an incretin hormone and neurotransmitter secreted from intestinal L-cells in response to nutrients to stimulate insulin and block glucagon secretion in a glucose-dependent manner. GLP-1 in itself is rapidly degraded, but long-acting GLP-1 receptor agonists (GLP-1 RAs) have become central in the treatment of T2D because of the beneficial effects extending also beyond glucose control. Currently, these therapeutics must be injected either daily or weekly or taken daily orally, leaving room for technological innovations that enable less frequent administrations, which will reduce patient burden and increase patient compliance. An ideal GLP-1 RA drug product would provide continuous therapy for upwards of four months from a single administration to match the cadence with which T2D patients typically visit their physician. In this work, we leveraged an injectable hydrogel depot technology to develop a long-acting GLP-1 RA drug product. By modulating the hydrogel properties to tune GLP-1 RA retention within the hydrogel depot, we engineered formulations capable of months-long GLP-1 RA delivery. Using a rat model of T2D, we confirmed that a single injection of hydrogel-based therapies exhibits sustained exposure of GLP-1 RA over 42 days, corresponding to a once-every four month therapy in humans. Moreover, these hydrogel therapies maintained optimal management of blood glucose and weight comparable to daily injections of a leading GLP-1 RA drug molecule. The pharmacokinetics and pharmacodynamics of these hydrogel-based long-acting GLP-1 RA treatments are promising for development of novel therapies reducing treatment burden for more effective management of T2D. Progress and Potential: While insufficient access to quality healthcare is problematic for consistent management of Type II diabetes (T2D), poor adherence to burdensome treatment regimens is one of the greatest challenges for disease management. Glucagon-like peptide 1 (GLP1) drugs have become central to the treatment of T2D due to their many beneficial effects beyond improving glucose control. Unfortunately, while optimization of GLP1 drugs has reduced treatment frequency from daily to weekly, significant patient burden still leads to poor patience compliance. In this work we developed an injectable hydrogel technology to enable GLP1 drugs only requiring administration once every four months. We showed in a rat model of T2D that one injection of a hydrogel-based therapy improves management of blood glucose and weight when compared with daily injections of the leading drug used clinically. These hydrogel-based GLP1 treatments are promising for reducing treatment burden and more effectively managing T2D. Future Impact: A GLP-1-based drug product providing four months of continuous therapy per administration could be transformational for the management of Type II diabetes (T2D). One of the most challenging aspects of diabetes management with GLP-1 mimics is maintenance of consistent levels of the drugs in the body, which is complicated by poor patient compliance on account of the high frequency of dosing required for current treatments. By leveraging a unique sustained release hydrogel depot technology we develop a months-long GLP-1 drug product candidate that has the potential to reduce patient burden and improving diabetes management. Overall, the hydrogel technology we describe here can dramatically reduce the frequency of therapeutic interventions, significantly increasing patient quality of life and reducing complications of diabetes management.Our next steps will focus on optimization of the drug formulations in a swine model of T2D, which is the most advanced and translationally-relevant animal model for these types of therapeutics. The long-term vision for this work is to translate lead candidate drug products towards clinical evaluation, which will also require comprehensive safety evaluation in multiple species and manufacturing our these materials according to Good Manufacturing Practices. The months-long-acting GLP-1 drug product that will come from this work has the potential to afford thus far unrealized therapeutic impact for the hundreds of millions of people with diabetes worldwide.
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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".