Understanding the Connection between Nanoparticle Uptake and Cancer Treatment Efficacy using Mathematical Modeling

Terisse A. Brocato, Eric N. Coker, Paul N. Durfee, Yu Shen Lin, Jason Townson, Edward F. Wyckoff, Vittorio Cristini, C. Jeffrey Brinker, Zhihui Wang

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Nanoparticles have shown great promise in improving cancer treatment efficacy while reducing toxicity and treatment side effects. Predicting the treatment outcome for nanoparticle systems by measuring nanoparticle biodistribution has been challenging due to the commonly unmatched, heterogeneous distribution of nanoparticles relative to free drug distribution. We here present a proof-of-concept study that uses mathematical modeling together with experimentation to address this challenge. Individual mice with 4T1 breast cancer were treated with either nanoparticle-delivered or free doxorubicin, with results demonstrating improved cancer kill efficacy of doxorubicin loaded nanoparticles in comparison to free doxorubicin. We then developed a mathematical theory to render model predictions from measured nanoparticle biodistribution, as determined using graphite furnace atomic absorption. Model analysis finds that treatment efficacy increased exponentially with increased nanoparticle accumulation within the tumor, emphasizing the significance of developing new ways to optimize the delivery efficiency of nanoparticles to the tumor microenvironment.

Original languageEnglish (US)
Article number7538
Pages (from-to)7538
JournalScientific Reports
Issue number1
StatePublished - May 24 2018

ASJC Scopus subject areas

  • General


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