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

Author:

Hsueh Henry T.,Chou Renee TiORCID,Rai Usha,Liyanage Wathsala,Kim Yoo Chun,Appell Matthew B.,Pejavar Jahnavi,Leo Kirby T.ORCID,Davison Charlotte,Kolodziejski Patricia,Mozzer Ann,Kwon HyeYoungORCID,Sista MaanasaORCID,Anders Nicole M.,Hemingway Avelina,Rompicharla Sri Vishnu Kiran,Edwards Malia,Pitha IanORCID,Hanes JustinORCID,Cummings Michael P.ORCID,Ensign Laura M.ORCID

Abstract

AbstractSustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We develop a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) is conjugated to brimonidine, an intraocular pressure lowering drug that is prescribed for three times per day topical dosing, intraocular pressure reduction is observed for up to 18 days after a single intracameral injection in rabbits. Further, the cumulative intraocular pressure lowering effect increases ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.

Funder

U.S. Department of Health & Human Services | National Institutes of Health

Research to Prevent Blindness

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary

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