Mathematical modeling in cancer nanomedicine: a review

Prashant Dogra; Joseph D. Butner; Yao li Chuang; Sergio Caserta; Shreya Goel; C. Jeffrey Brinker; Vittorio Cristini; Zhihui Wang

doi:10.1007/s10544-019-0380-2

Mathematical modeling in cancer nanomedicine: a review

Prashant Dogra, Joseph D. Butner, Yao li Chuang, Sergio Caserta, Shreya Goel, C. Jeffrey Brinker, Vittorio Cristini, Zhihui Wang

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

88 Scopus citations

Abstract

Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy.

Original language	English (US)
Article number	40
Journal	Biomedical Microdevices
Volume	21
Issue number	2
DOIs	https://doi.org/10.1007/s10544-019-0380-2
State	Published - Jun 1 2019

Keywords

Agent-based modeling
Cancer treatment
Drug transport
Mechanistic modeling
Multiscale
Pharmacokinetics and pharmacodynamics

ASJC Scopus subject areas

Biomedical Engineering
Molecular Biology

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10.1007/s10544-019-0380-2

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title = "Mathematical modeling in cancer nanomedicine: a review",

abstract = "Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy.",

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author = "Prashant Dogra and Butner, {Joseph D.} and Chuang, {Yao li} and Sergio Caserta and Shreya Goel and Brinker, {C. Jeffrey} and Vittorio Cristini and Zhihui Wang",

note = "Funding Information: This research has been supported in part by the National Science Foundation Grant DMS-1716737 (VC, ZW), the National Institutes of Health (NIH) Grants 1U01CA196403 (VC, ZW), 1U01CA213759 (VC, ZW), 1R01CA226537 (CJB, VC, ZW), 1R01CA222007 (VC, ZW), U54CA210181 (VC, ZW), and the University of Texas System STARS Award (VC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CJB acknowledges additional support from the Sandia National Laboratories LDRD program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy{\textquoteright}s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Publisher Copyright: {\textcopyright} 2019, The Author(s). Copyright: Copyright 2019 Elsevier B.V., All rights reserved.",

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doi = "10.1007/s10544-019-0380-2",

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AU - Butner, Joseph D.

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AU - Caserta, Sergio

AU - Goel, Shreya

AU - Brinker, C. Jeffrey

AU - Cristini, Vittorio

AU - Wang, Zhihui

N1 - Funding Information: This research has been supported in part by the National Science Foundation Grant DMS-1716737 (VC, ZW), the National Institutes of Health (NIH) Grants 1U01CA196403 (VC, ZW), 1U01CA213759 (VC, ZW), 1R01CA226537 (CJB, VC, ZW), 1R01CA222007 (VC, ZW), U54CA210181 (VC, ZW), and the University of Texas System STARS Award (VC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CJB acknowledges additional support from the Sandia National Laboratories LDRD program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Publisher Copyright: © 2019, The Author(s). Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

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N2 - Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy.

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Mathematical modeling in cancer nanomedicine: a review

Abstract

Keywords

ASJC Scopus subject areas

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