Multi-Stimuli-Responsive Nanoparticles Formed of POSS-PEG for the Delivery of Boronic Acid-Containing Therapeutics.

Polymeric vehicles often exhibit batch-to-batch variations due to polydispersity, limiting their reproducibility for biomedical applications. In contrast, polyhedral oligomeric silsesquioxane (POSS) has emerged as an attractive candidate for drug delivery due to its precise chemical structure and rigid molecular shape. A promising strategy to enhance drug efficacy while reducing systemic toxicity is the development of multi-stimuli-responsive delivery systems capable of targeted drug release at a disease site. Herein, we developed a drug delivery platform based on POSS-polymer conjugates. By functionalizing the POSS with amino groups and establishing B-N coordination with boronic acids, the nanoparticles (NPs) exhibit responsive behavior to stimuli, including adenosine-5'-triphosphate (ATP), acidic pH, and nucleophilic reagents. We successfully encapsulated two boronic acid-containing molecules: tetraphenylethylene (TPE), serving as a fluorescent probe, and bortezomib (BTZ), an anticancer drug. The TPE@NPs were employed to visualize the cellular uptake of NPs by tumor cells, while the BTZ@NPs exhibited increased cytotoxicity in tumor cells compared with normal cells. This POSS-PEG conjugate offers a nanoparticle platform for encapsulating versatile boronic acid-containing molecules, thereby enhancing drug efficacy while minimizing systemic toxicity. Given the wide-ranging applications of boronic acid-containing molecules in biomedicine, our platform holds significant promise for the development of intelligent drug delivery systems for diagnostics and therapeutics.

Design of self-assembly dipeptide hydrogels and machine learning via their chemical features.

Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure-property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.