Competitive Inhibition as a Tool to Modulate and Predict Dynamic Hydrogel Mechanics

Dynamic hydrogels are powerful biomaterials whose performance in drug delivery, tissue engineering, and related applications depends on mechanical properties that remain difficult to predict. We introduce a simple, quantitative framework for tuning hydrogel mechanics through competitive inhibition, where small-molecule competitors reversibly disrupt cross-linking. Inspired by Michaelis-Menten kinetics, the model defines an apparent cross-link association constant, K _a,app, that decreases as a function of competitor concentration and binding affinity. Incorporating K _a,app into traditional network theory enabled quantitative prediction of modulus. When using boronate ester networks and small-molecule competitors bearing diol motifs spanning 4 orders of magnitude in affinity, the predicted and measured moduli agreed within 10% relative error. A Langmuir-type decay function further captured stress-relaxation behavior by accounting for changes in effective cross-link density. Extending the approach to hydrazone-cross-linked gels confirmed its generality across distinct dynamic chemistries and exchange mechanisms. Finally, we demonstrate practical relevance by transforming a nonextrudable gel into a hand-injectable material through competitor addition. This framework establishes competitive inhibition as a universal and predictive strategy for designing adaptive soft materials.