Toward Cell-Specific Nanoparticle Delivery Systems
The targetable delivery of therapeutic nanoparticles remains a significant challenge in modern medicine, particularly due to the complexity, time, and expense involved in experimental design and optimization for cell-specific applications. To address this, NOCAP (Nanoparticle Optimization and Cell A...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156308 |
| _version_ | 1811098198334242816 |
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| author | Murphy, Sean |
| author2 | Barzilay, Regina |
| author_facet | Barzilay, Regina Murphy, Sean |
| author_sort | Murphy, Sean |
| collection | MIT |
| description | The targetable delivery of therapeutic nanoparticles remains a significant challenge in modern medicine, particularly due to the complexity, time, and expense involved in experimental design and optimization for cell-specific applications. To address this, NOCAP (Nanoparticle Optimization and Cell Affinity Prediction) was developed, a computational framework designed to (i) predict the affinities between nanoparticles and gene expression signatures of cancer cells and (ii) optimize nanoparticle formulations for specific targets. NOCAP successfully predicts cellular affinity for previously unseen cancer cell lines. The findings demonstrate the potential of machine learning to streamline the rational selection of target-specific nanoparticle drug delivery systems, paving the way for more efficient and precise therapeutic interventions. |
| first_indexed | 2024-09-23T17:11:26Z |
| format | Thesis |
| id | mit-1721.1/156308 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T17:11:26Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1563082024-08-22T03:00:52Z Toward Cell-Specific Nanoparticle Delivery Systems Murphy, Sean Barzilay, Regina Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science The targetable delivery of therapeutic nanoparticles remains a significant challenge in modern medicine, particularly due to the complexity, time, and expense involved in experimental design and optimization for cell-specific applications. To address this, NOCAP (Nanoparticle Optimization and Cell Affinity Prediction) was developed, a computational framework designed to (i) predict the affinities between nanoparticles and gene expression signatures of cancer cells and (ii) optimize nanoparticle formulations for specific targets. NOCAP successfully predicts cellular affinity for previously unseen cancer cell lines. The findings demonstrate the potential of machine learning to streamline the rational selection of target-specific nanoparticle drug delivery systems, paving the way for more efficient and precise therapeutic interventions. S.M. 2024-08-21T18:55:38Z 2024-08-21T18:55:38Z 2024-05 2024-07-10T12:59:47.191Z Thesis https://hdl.handle.net/1721.1/156308 Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Murphy, Sean Toward Cell-Specific Nanoparticle Delivery Systems |
| title | Toward Cell-Specific Nanoparticle Delivery Systems |
| title_full | Toward Cell-Specific Nanoparticle Delivery Systems |
| title_fullStr | Toward Cell-Specific Nanoparticle Delivery Systems |
| title_full_unstemmed | Toward Cell-Specific Nanoparticle Delivery Systems |
| title_short | Toward Cell-Specific Nanoparticle Delivery Systems |
| title_sort | toward cell specific nanoparticle delivery systems |
| url | https://hdl.handle.net/1721.1/156308 |
| work_keys_str_mv | AT murphysean towardcellspecificnanoparticledeliverysystems |