The Mechanism of Celosiae Semen in the Treatment of Diabetic Cataract: Based on Network Pharmacology.

INTRODUCTION

Diabetes mellitus can be complicated by a variety of ocular diseases, among which the postoperative complications of diabetic cataract (DC) are significantly higher than those of non-DC patients. Therefore, finding drugs with natural active ingredients is an urgent challenge in the prevention and treatment of DC. Discovering the potential molecular mechanism of celosiae semen (CS) for the treatment of DC and providing new ideas and programs for the treatment and prevention of DC.

METHODS

In this study, network pharmacology, molecular docking, and molecular dynamics simulations were utilized to predict the binding and functional enrichment of the main active ingredients of CS with DC-related targets, and to explore the potential pathways and mechanisms of CS for the treatment of DC.

RESULTS

Through database searching and screening, a total of 45 potential targets of CS for the treatment of DC were identified, functionally enriched, and a protein-protein interaction network was constructed, and the key target, SRC, was finally found. The results of molecular docking and molecular dynamics simulation showed that the main active ingredient of CS, stigmasterol, could bind stably to the key target SRC protein.

DISCUSSION: This study not only elucidates the phyto-pharmacological basis of CS in DC management but also provides a framework for developing natural product-derived targeted therapies against diabetic ocular complications. The integration of modern genomics and computational chemistry to deconstruct the therapeutic effects of traditional Chinese herbal medicines has great clinical significance in expanding the scope of traditional Chinese medicines for the treatment of DC and promoting precision targeting. However, this requires verification through basic experiments.

CONCLUSION

These computational findings suggest that CS may exert its anti-cataract effects through the multi-target modulation of diabetic metabolic pathways and SRC-mediated signaling cascades.