Competitive Inhibition as a Tool to Modulate and Predict Hydrogel Mechanics

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 G_p and relaxation time 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 G_p 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.