Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery.

Area of Science:

• Ophthalmology
• Biomaterials Science
• Drug Delivery

Background:

• Chronic ocular diseases pose treatment challenges due to poor patient adherence to eye drops and frequent intraocular injections.
• Developing sustained drug delivery systems for the eye is crucial for effective long-term disease management.

Purpose of the Study:

• To engineer multifunctional peptides with melanin-binding properties for sustained drug delivery in the eye.
• To create peptide-drug conjugates that act as a localized, long-acting therapeutic depot.

Main Methods:

• Utilized a super learning-based methodology to design multifunctional peptides.
• Engineered peptides for cellular entry, melanin binding, and low cytotoxicity.
• Conjugated the lead peptide (HR97) to brimonidine, an intraocular pressure-lowering drug.

Main Results:

• A single intracameral injection of the brimonidine-conjugated peptide in rabbits demonstrated intraocular pressure reduction for up to 18 days.
• The sustained-release formulation achieved an approximately 17-fold increase in cumulative intraocular pressure lowering effect compared to free brimonidine.
• The engineered peptide exhibited efficient cell entry, melanin binding, and low cytotoxicity.

Conclusions:

• Multifunctional peptide-drug conjugates offer a promising strategy for sustained therapeutic delivery in ocular applications.
• This approach addresses limitations of current treatments for chronic eye diseases, potentially improving patient outcomes.
• The engineered peptide-drug conjugate system shows potential for sustained delivery beyond ocular applications.