Engineering Ultra-Small Cerium-Based Metal-Organic Frameworks Nanozymes for Efficient Antioxidative Treatment of Dry Eye Disease

Dry eye disease (DED) is a widespread ophthalmic illness that inflicts massive economic and medical burdens on society. Although oxidative stress is closely associated with the onset of DED, barely any quantities of antioxidants are clinically available owing to low efficiency, toxicity, and poor bioavailability. Cerium-based nanozymes possess promising enzyme-mimetic activities in scavenging reactive oxygen species (ROS). Moreover, the ultra-small metal-organic frameworks (MOFs) have the potential for more efficient drug delivery, ROS elimination, and therapeutic efficacy. However, the development of ultra-small cerium-based MOFs (Ce-MOFs) is limited to a small minority due to challenges in the synthetic process and stability. By simply adjusting raw ingredients concentrations, three types of Ce-MOFs with distinct particle sizes are synthesized: Ce-MOF 1 (213 nm), Ce-MOF 2 (36 nm), and Ce-MOF 3 (2 nm). In comparison, the obtained ultra-small Ce-MOF 3 exhibited superior ROS scavenging and antioxidant capacity, decreased cytotoxicity, and excellent ocular penetration. Therapeutically, Ce-MOF 3 is demonstrated to be highly efficient in alleviating DED by suppressing oxidative stress and inflammation, boosting corneal epithelial repair, and facilitating tear secretion recovery. The novel ultra-small Ce-MOF 3 may serve as a promising alternative treatment for DED and other ROS-related disorders when combined with ocular biocompatibility. By simply adjusting the concentrations of raw ingredients, the cerium-based metal-organic frameworks (Ce-MOFs) nanozymes with ultra-small particle size (2-3 nm) are first synthesized. In comparison, the obtained ultra-small Ce-MOFs nanozymes exhibited superior antioxidative capacity and decreased cytotoxicity. Therapeutically, the novel Ce-MOFs nanozymes are demonstrated to be a highly efficient and promising therapeutic candidate for dry eye disease treatment.image