Toward Predicting Nanoparticle Distribution in Heterogeneous Tumor Tissues

Presley MacMillan; Abdullah M. Syed; Benjamin R. Kingston; Jessica Ngai; Shrey Sindhwani; Zachary P. Lin; Luan N.M. Nguyen; Wayne Ngo; Stefan M. Mladjenovic; Qin Ji; Colin Blackadar; Warren C.W. Chan

doi:10.1021/acs.nanolett.3c02186

Toward Predicting Nanoparticle Distribution in Heterogeneous Tumor Tissues

Presley MacMillan, Abdullah M. Syed, Benjamin R. Kingston, Jessica Ngai, Shrey Sindhwani, Zachary P. Lin, Luan N.M. Nguyen, Wayne Ngo, Stefan M. Mladjenovic, Qin Ji, Colin Blackadar, Warren C.W. Chan^*

^*Corresponding author for this work

University of Toronto

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	7197-7205
Number of pages	9
Journal	Nano Letters
Volume	23
Issue number	15
DOIs	https://doi.org/10.1021/acs.nanolett.3c02186
Publication status	Published - Aug 9 2023
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2023 American Chemical Society.

ASJC Scopus Subject Areas

Bioengineering
General Chemistry
General Materials Science
Condensed Matter Physics
Mechanical Engineering

Keywords

cancer
drug delivery
heterogeneity
image analysis
machine learning
nanoparticles

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1021/acs.nanolett.3c02186

Cite this

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title = "Toward Predicting Nanoparticle Distribution in Heterogeneous Tumor Tissues",

abstract = "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.",

keywords = "cancer, drug delivery, heterogeneity, image analysis, machine learning, nanoparticles",

author = "Presley MacMillan and Syed, \{Abdullah M.\} and Kingston, \{Benjamin R.\} and Jessica Ngai and Shrey Sindhwani and Lin, \{Zachary P.\} and Nguyen, \{Luan N.M.\} and Wayne Ngo and Mladjenovic, \{Stefan M.\} and Qin Ji and Colin Blackadar and Chan, \{Warren C.W.\}",

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year = "2023",

month = aug,

day = "9",

doi = "10.1021/acs.nanolett.3c02186",

language = "English",

volume = "23",

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T1 - Toward Predicting Nanoparticle Distribution in Heterogeneous Tumor Tissues

AU - MacMillan, Presley

AU - Syed, Abdullah M.

AU - Kingston, Benjamin R.

AU - Ngai, Jessica

AU - Sindhwani, Shrey

AU - Lin, Zachary P.

AU - Nguyen, Luan N.M.

AU - Ngo, Wayne

AU - Mladjenovic, Stefan M.

AU - Ji, Qin

AU - Blackadar, Colin

AU - Chan, Warren C.W.

PY - 2023/8/9

Y1 - 2023/8/9

N2 - 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.

AB - 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.

KW - cancer

KW - drug delivery

KW - heterogeneity

KW - image analysis

KW - machine learning

KW - nanoparticles

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Toward Predicting Nanoparticle Distribution in Heterogeneous Tumor Tissues

Abstract

Bibliographical note

ASJC Scopus Subject Areas

Keywords

UN SDGs

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