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

Author

Listed:
  • Henry T. Hsueh

    (Johns Hopkins University School of Medicine
    Johns Hopkins University)

  • Renee Ti Chou

    (University of Maryland, College Park)

  • Usha Rai

    (Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine)

  • Wathsala Liyanage

    (Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine)

  • Yoo Chun Kim

    (Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine)

  • Matthew B. Appell

    (Johns Hopkins University School of Medicine
    Johns Hopkins University)

  • Jahnavi Pejavar

    (Johns Hopkins University School of Medicine
    Johns Hopkins University)

  • Kirby T. Leo

    (Johns Hopkins University School of Medicine
    Johns Hopkins University)

  • Charlotte Davison

    (Johns Hopkins University School of Medicine
    Johns Hopkins University)

  • Patricia Kolodziejski

    (Johns Hopkins University School of Medicine
    Johns Hopkins University)

  • Ann Mozzer

    (Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine)

  • HyeYoung Kwon

    (Johns Hopkins University School of Medicine
    Johns Hopkins University)

  • Maanasa Sista

    (Johns Hopkins University School of Medicine
    Case Western Reserve University)

  • Nicole M. Anders

    (The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University)

  • Avelina Hemingway

    (The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University)

  • Sri Vishnu Kiran Rompicharla

    (Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine)

  • Malia Edwards

    (Johns Hopkins University School of Medicine)

  • Ian Pitha

    (Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine)

  • Justin Hanes

    (Johns Hopkins University School of Medicine
    Johns Hopkins University
    Johns Hopkins University School of Medicine
    Johns Hopkins University)

  • Michael P. Cummings

    (University of Maryland, College Park)

  • Laura M. Ensign

    (Johns Hopkins University School of Medicine
    Johns Hopkins University
    Johns Hopkins University School of Medicine
    Johns Hopkins University)

Abstract

Sustained 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.

Suggested Citation

  • Henry T. Hsueh & Renee Ti Chou & Usha Rai & Wathsala Liyanage & Yoo Chun Kim & Matthew B. Appell & Jahnavi Pejavar & Kirby T. Leo & Charlotte Davison & Patricia Kolodziejski & Ann Mozzer & HyeYoung Kw, 2023. "Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38056-w
    DOI: 10.1038/s41467-023-38056-w
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    References listed on IDEAS

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    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
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    Cited by:

    1. Weijia Liu & Zhijian Du & Zhongyi Duan & La Li & Guozhen Shen, 2024. "Neuroprosthetic contact lens enabled sensorimotor system for point-of-care monitoring and feedback of intraocular pressure," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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