Year: 2026
Type: article
Abstract: Dynamic hydrogels have emerged as powerful biomaterials, but their performance in applications such as drug delivery or tissue engineering hinges on distinct mechanical properties that remain difficult to predict. Employing small-molecule competitors that disrupt crosslinking provides a versatile tool to modulate these properties, yet no framework exists to quantitatively predict the resulting changes. To address this challenge, we developed a simple framework inspired by Michaelis-Menten competitive inhibition that quantitatively predicts hydrogel plateau modulus (Gp) and relaxation time (τ) in the presence of competitors. The approach was validated in ideal poly(ethylene glycol) networks crosslinked by boronate esters or hydrazones with competitors spanning four orders of magnitude in affinity. Linking Gp to functional outcomes, we further show that the framework captures trends in injectability, with competitors transforming gels from non-extrudable to hand-injectable. These results position competitive binding as a predictive design tool to expand the tunability of dynamic hydrogels.
Source: ACS Central Science
Institution: Colorado State University
Language: English
Subfield: Molecular Medicine
Domain: Life Sciences
Open Access status: diamond
Award: G2372392654
OpenAlex ID: W7129272207