The Strategies of Development of New Non-Toxic Inhibitors of Amyloid Formation

Converting Short-Acting Insulin into Thermo-Stable Longer-Acting Insulin Using Multi-Layer Detachable Microneedles

The detachable dissolving microneedles (DDMNs) feature an array of needles capable of being separated from the base sheet during administration. Here they were fabricated to address delivery efficiency and storage stability of insulin. The constructed insulin-DDMN is multi-layered, with 1) a hard tip cover layer; 2) a layer of regular short-acting insulin (RI) mixed with hyaluronic acid (HA) and sorbitol (Sor) which occupies the taper tip region of the needles; 3) a barrier layer situated above the RI layer; and 4) a fast-dissolving layer connecting the barrier layer to the base sheet. RI entrapped in DDMNs exhibited enhanced thermal stability; it could be stored at 40 °C for 35 days without losing significant biological activity. Differential scanning calorimetric analysis revealed that the HA-Sor matrix could improve the denaturation temperature of the RI from lower than room temperature to 186 °C. Tests in ex vivo porcine skin demonstrated RI delivery efficiency of 91±1.59 %. Experiments with diabetic rats revealed sustained release of RI, i.e., when compared to subcutaneous injection with the same RI dose, RI-DDMNs produced slower absorption of insulin into blood circulation, delayed onset of hypoglycemic effect, longer serum insulin half-life, and longer hypoglycemic duration.

Characterizing fibril morphological changes by spirooxindoles for neurodegenerative disease application

Fibrillation of proteins and polypeptides, which leads to the deposition of plaques in cells and tissues has been widely associated with many neuropathological diseases. Inhibition of protein misfolding and aggregation is crucial for the prevention and treatment of these conditions. The growing interest in identifying inhibitor molecules to prevent the formation of fibrils in vivo has led to the results highlighted in this study. Due to their hydrophobic structure and potential to readily cross the blood brain barrier, a library of spirooxindole compounds were synthesized with those labelled Hd-63, Hd-66 and Hd-74 proving to be the most potent against fibril formation. Our spectroscopic analysis provides detailed insight, that the introduction of these spirooxindole compounds leads to morphological changes in the mechanism of fibril formation which prevent the formation of highly ordered fibrils, instead results in the formation of disordered aggregates which are not fibrillar in nature.

Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.