Supplementary materials for machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery
dc.contributor.advisor | Cummings, Michael P. | |
dc.contributor.advisor | Ensign, Laura M. | |
dc.contributor.author | Chou, Renee Ti | |
dc.contributor.author | Hsueh, Henry T. | |
dc.contributor.author | Rai, Usha | |
dc.contributor.author | Liyanage, Wathsala | |
dc.contributor.author | Kim, Yoo Chun | |
dc.contributor.author | Appell, Matthew B. | |
dc.contributor.author | Pejavar, Jahnavi | |
dc.contributor.author | Leo, Kirby T. | |
dc.contributor.author | Davison, Charlotte | |
dc.contributor.author | Kolodziejski, Patricia | |
dc.contributor.author | Mozzer, Ann | |
dc.contributor.author | Kwon, HyeYoung | |
dc.contributor.author | Sista, Maanasa | |
dc.contributor.author | Anders, Nicole M. | |
dc.contributor.author | Hemingway, Avelina | |
dc.contributor.author | Rompicharla, Sri Vishnu Kiran | |
dc.contributor.author | Edwards, Malia | |
dc.contributor.author | Pitha, Ian | |
dc.contributor.author | Hanes, Justin | |
dc.contributor.author | Cummings, Michael P. | |
dc.contributor.author | Ensign, Laura M. | |
dc.date.accessioned | 2023-01-27T22:09:47Z | |
dc.date.available | 2023-01-27T22:09:47Z | |
dc.date.issued | 2023 | |
dc.description | The research notebook contains the code of the machine learning pipeline developed in the project, which involves a super learner-based methodology for multifunctional peptide engineering. The main pipeline consists of variable reduction and model training. The code functionality details can be found in the Methods section of the paper "Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery." | en_US |
dc.description.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 developed 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) was conjugated to brimonidine, an intraocular pressure (IOP)-lowering drug that is prescribed for three times per day topical dosing, IOP reduction was observed for up to 18 days after a single intracameral HR97-brimonidine injection in rabbits. Further, the cumulative IOP-lowering effect was increased ~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. | en_US |
dc.description.sponsorship | National Institutes of Health (NIH) (grant nos. R01EY026578 and R01EY031041; National Eye Institute Training Grant (T32EY007143); National Science Foundation Award (DGE-1632976) | en_US |
dc.identifier | https://doi.org/10.13016/0jck-hnnv | |
dc.identifier.uri | http://hdl.handle.net/1903/29529 | |
dc.language.iso | en_US | en_US |
dc.relation.isAvailableAt | Library Research & Innovative Practice Forum | |
dc.relation.isAvailableAt | Digital Repository at the University of Maryland | |
dc.relation.isAvailableAt | University of Maryland (College Park, Md) | |
dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
dc.subject | machine learning | en_US |
dc.subject | drug delivery | en_US |
dc.title | Supplementary materials for machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery | en_US |
dc.type | Software | en_US |
Files
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- Name:
- main_notebook.zip
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- 98.38 MB
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- Research notebook containing the code for implementing the machine learning algorithms and manuscript figure generation.
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- other_files.zip
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- 999.32 MB
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- Description:
- Other files containing the input data sets, intermediate data generated from the ML pipeline, and the source code for generating the research notebook.
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- Name:
- rdata.zip
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- 737.15 MB
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- Description:
- RData files containing the data generated from the ML pipeline. Once downloaded, move the decompressed folder into `other_files` to run the software.
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- Name:
- final_models.zip
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- 19.56 MB
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- Description:
- Final property models