A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors
Targeted nanoparticles are increasingly being engineered for the treatment of cancer. By design, they can passively accumulate in tumors, selectively bind to targets in their environment, and deliver localized treatments. However, the penetration of targeted nanoparticles deep into tissue can be hin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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Elsevier
2015
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| Online Access: | http://hdl.handle.net/1721.1/100420 https://orcid.org/0000-0002-1293-2097 |
| _version_ | 1826204850785878016 |
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| author | Hauert, Sabine Berman, Spring Nagpal, Radhika Bhatia, Sangeeta N |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Hauert, Sabine Berman, Spring Nagpal, Radhika Bhatia, Sangeeta N |
| author_sort | Hauert, Sabine |
| collection | MIT |
| description | Targeted nanoparticles are increasingly being engineered for the treatment of cancer. By design, they can passively accumulate in tumors, selectively bind to targets in their environment, and deliver localized treatments. However, the penetration of targeted nanoparticles deep into tissue can be hindered by their slow diffusion and a high binding affinity. As a result, they often localize to areas around the vessels from which they extravasate, never reaching the deep-seeded tumor cells, thereby limiting their efficacy. To increase tissue penetration and cellular accumulation, we propose generalizable guidelines for nanoparticle design and validate them using two different computer models that capture the potency, motion, binding kinetics, and cellular internalization of targeted nanoparticles in a section of tumor tissue. One strategy that emerged from the models was delaying nanoparticle binding until after the nanoparticles have had time to diffuse deep into the tissue. Results show that nanoparticles that are designed according to these guidelines do not require fine-tuning of their kinetics or size and can be administered in lower doses than classical targeted nanoparticles for a desired tissue penetration in a large variety of tumor scenarios. In the future, similar models could serve as a testbed to explore engineered tissue-distributions that arise when large numbers of nanoparticles interact in a tumor environment. |
| first_indexed | 2024-09-23T13:02:04Z |
| format | Article |
| id | mit-1721.1/100420 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:02:04Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1004202022-09-28T11:38:47Z A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors Hauert, Sabine Berman, Spring Nagpal, Radhika Bhatia, Sangeeta N Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Koch Institute for Integrative Cancer Research at MIT Hauert, Sabine Bhatia, Sangeeta N. Targeted nanoparticles are increasingly being engineered for the treatment of cancer. By design, they can passively accumulate in tumors, selectively bind to targets in their environment, and deliver localized treatments. However, the penetration of targeted nanoparticles deep into tissue can be hindered by their slow diffusion and a high binding affinity. As a result, they often localize to areas around the vessels from which they extravasate, never reaching the deep-seeded tumor cells, thereby limiting their efficacy. To increase tissue penetration and cellular accumulation, we propose generalizable guidelines for nanoparticle design and validate them using two different computer models that capture the potency, motion, binding kinetics, and cellular internalization of targeted nanoparticles in a section of tumor tissue. One strategy that emerged from the models was delaying nanoparticle binding until after the nanoparticles have had time to diffuse deep into the tissue. Results show that nanoparticles that are designed according to these guidelines do not require fine-tuning of their kinetics or size and can be administered in lower doses than classical targeted nanoparticles for a desired tissue penetration in a large variety of tumor scenarios. In the future, similar models could serve as a testbed to explore engineered tissue-distributions that arise when large numbers of nanoparticles interact in a tumor environment. Human Frontier Science Program (Strasbourg, France) David H. Koch Institute for Integrative Cancer Research at MIT (Marie D. and Pierre Casimir-Lambert Fund) National Institutes of Health (U.S.) (Grant U54 CA151884) National Cancer Institute (U.S.) (Koch Institute Support (Core) Grant P30-CA14051) 2015-12-18T01:59:17Z 2015-12-18T01:59:17Z 2013-12 2013-11 Article http://purl.org/eprint/type/JournalArticle 17480132 http://hdl.handle.net/1721.1/100420 Hauert, Sabine, Spring Berman, Radhika Nagpal, and Sangeeta N. Bhatia. “A Computational Framework for Identifying Design Guidelines to Increase the Penetration of Targeted Nanoparticles into Tumors.” Nano Today 8, no. 6 (December 2013): 566–576. https://orcid.org/0000-0002-1293-2097 en_US http://dx.doi.org/10.1016/j.nantod.2013.11.001 Nano Today Creative Commons Attribution http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier PMC |
| spellingShingle | Hauert, Sabine Berman, Spring Nagpal, Radhika Bhatia, Sangeeta N A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors |
| title | A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors |
| title_full | A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors |
| title_fullStr | A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors |
| title_full_unstemmed | A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors |
| title_short | A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors |
| title_sort | computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors |
| url | http://hdl.handle.net/1721.1/100420 https://orcid.org/0000-0002-1293-2097 |
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