Engineering glucose-responsive insulin

From small changes to big gains: pyridinium-based tetralactam macrocycle for enhanced sugar recognition in water

The complex distribution of functional groups in carbohydrates, coupled with their strong solvation in water, makes them challenging targets for synthetic receptors. Despite extensive research into various molecular frameworks, most synthetic carbohydrate receptors have exhibited low affinities, and their interactions with sugars in aqueous environments remain poorly understood. In this work, we present a simple pyridinium-based hydrogen-bonding receptor derived from a subtle structural modification of a well-known tetralactam macrocycle. This small structural change resulted in a dramatic enhancement of glucose binding affinity, increasing from 56 M-1 to 3001 M-1. Remarkably, the performance of our synthetic lectin surpasses that of the natural lectin, concanavalin A, by over fivefold. X-ray crystallography of the macrocycle-glucose complex reveals a distinctive hydrogen bonding pattern, which allows for a larger surface overlap between the receptor and glucose, contributing to the enhanced affinity. Furthermore, this receptor possesses allosteric binding sites, which involve chloride binding and trigger receptor aggregation. This unique allosteric process reveals the critical role of structural flexibility in this hydrogen-bonding receptor for the effective recognition of sugars. We also demonstrate the potential of this synthetic lectin as a highly sensitive glucose sensor in aqueous solutions.

Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?

Research in the treatment of type 1 diabetes has been addressed into two main areas: the development of "intelligent insulins" capable of auto-regulating their own levels according to glucose concentrations, or the exploitation of artificial intelligence (AI) and its learning capacity, to provide decision support systems to improve automated insulin therapy. This review aims to provide a synthetic overview of the current state of these two research areas, providing an outline of the latest development in the search for "intelligent insulins," and the results of new and promising advances in the use of artificial intelligence to regulate automated insulin infusion and glucose control. The future of insulin treatment in type 1 diabetes appears promising with AI, with research nearly reaching the possibility of finally having a "closed-loop" artificial pancreas.

Targeting pancreatic β cells for diabetes treatment

Insulin is a life-saving drug for patients with type 1 diabetes; however, even today, no pharmacotherapy can prevent the loss or dysfunction of pancreatic insulin-producing β cells to stop or reverse disease progression. Thus, pancreatic β cells have been a main focus for cell-replacement and regenerative therapies as a curative treatment for diabetes. In this Review, we highlight recent advances toward the development of diabetes therapies that target β cells to enhance proliferation, redifferentiation and protection from cell death and/or enable selective killing of senescent β cells. We describe currently available therapies and their mode of action, as well as insufficiencies of glucagon-like peptide 1 (GLP-1) and insulin therapies. We discuss and summarize data collected over the last decades that support the notion that pharmacological targeting of β cell insulin signalling might protect and/or regenerate β cells as an improved treatment of patients with diabetes.

"Smart" Composite Microneedle Patch Stabilizes Glucagon and Prevents Nocturnal Hypoglycemia: Experimental Studies and Molecular Dynamics Simulation

Hypoglycemia is a major complication associated with insulin therapy in people with diabetes that could cause life-threatening conditions if untreated. Glucagon, a counter-acting hormone, is thus administered for rescue of severe hypoglycemia. However, due to the instability of glucagon, only limited medications are available for emergency use, which are unsuitable for patients with hypoglycemia unawareness or with the inability to self-administer, especially during sleep (namely, nocturnal hypoglycemia). To prevent unattended and extended hypoglycemia, we designed a "smart" composite microneedle (cMN) patch capable of stabilizing glucagon, sensing hypoglycemia, and delivering glucagon automatically on demand. In this design, native glucagon was encapsulated in glucose-responsive microgels containing a glucagon-stabilizing component rationally selected by molecular dynamics (MD) simulation. A cMN patch was then prepared by incorporating the glucagon microgels with poly(methyl vinyl ether-alt-maleic anhydride) (PMVE-MAH) and poly(ethylene glycol) (PEG) followed by thermal cross-linking. The rationally designed zwitterionic polymer-based microgels preserved the native structure of glucagon and prevented heat-induced fibrillation evidenced by RP-HPLC, circular dichroism, and transmission electron microscopy. MD simulations suggested that the polymeric microgels stabilized glucagon by inhibition of oligomer formation via peptide-polymer noncovalent interactions. The polymer formed multiple hydrogen bonds with the polar and charged amino acid residues of the glucagon molecule, shielding the peptide surface from aggregation. In vivo efficacy studies using streptozotocin-induced type 1 diabetic (T1D) rats demonstrated that the glucagon-loaded cMN patch could prevent hypoglycemia induced by insulin overdose during a 12 h period. The results suggest that this new glucagon "smart" patch may be a promising system for improving the quality of life of those suffering from nocturnal hypoglycemia and hypoglycemia unawareness.