Toward Predicting Nanoparticle Distribution in Heterogeneous Tumor Tissues
Nanobio interaction studies have generated a significant amount of data. An important next step is to organize the data and design computational techniques to analyze the nanobio interactions. Here we developed a computational technique to correlate the nanoparticle spatial distribution within heter...
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| Veröffentlicht in: | Nano letters 2023-08, Vol.23 (15), p.7197-7205 |
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| container_title | Nano letters |
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| creator | MacMillan, Presley Syed, Abdullah M. Kingston, Benjamin R. Ngai, Jessica Sindhwani, Shrey Lin, Zachary P. Nguyen, Luan N. M. Ngo, Wayne Mladjenovic, Stefan M. Ji, Qin Blackadar, Colin Chan, Warren C. W. |
| description | Nanobio interaction studies have generated a significant amount of data. An important next step is to organize the data and design computational techniques to analyze the nanobio interactions. Here we developed a computational technique to correlate the nanoparticle spatial distribution within heterogeneous solid tumors. This approach led to greater than 88% predictive accuracy of nanoparticle location within a tumor tissue. This proof-of-concept study shows that tumor heterogeneity might be defined computationally by the patterns of biological structures within the tissue, enabling the identification of tumor patterns for nanoparticle accumulation. |
| doi_str_mv | 10.1021/acs.nanolett.3c02186 |
| format | Article |
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| source | ACS Publications; MEDLINE |
| subjects | Humans Nanoparticles - chemistry Neoplasms |
| title | Toward Predicting Nanoparticle Distribution in Heterogeneous Tumor Tissues |
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