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Sci Rep. 2017 Jul 18;7(1):5725. doi: 10.1038/s41598-017-05902-z.

A Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast Cancer.

Author information

1
Vanderbilt University Institute of Imaging Science, Nashville, USA.
2
Department of Biomedical Engineering, Vanderbilt University, Nashville, USA.
3
Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA.
4
Livestrong Cancer Institutes, The University of Texas at Austin, Austin, USA.
5
Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, USA.
6
Department of Radiology & Radiological Sciences, Vanderbilt University School of Medicine, Nashville, USA.
7
Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA. thomas.yankeelov@utexas.edu.
8
Department of Diagnostic Medicine, Dell Medical School, The University of Texas at Austin, Austin, USA. thomas.yankeelov@utexas.edu.
9
Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, USA. thomas.yankeelov@utexas.edu.
10
Livestrong Cancer Institutes, The University of Texas at Austin, Austin, USA. thomas.yankeelov@utexas.edu.

Abstract

Doxorubicin forms the basis of chemotherapy regimens for several malignancies, including triple negative breast cancer (TNBC). Here, we present a coupled experimental/modeling approach to establish an in vitro pharmacokinetic/pharmacodynamic model to describe how the concentration and duration of doxorubicin therapy shape subsequent cell population dynamics. This work features a series of longitudinal fluorescence microscopy experiments that characterize (1) doxorubicin uptake dynamics in a panel of TNBC cell lines, and (2) cell population response to doxorubicin over 30 days. We propose a treatment response model, fully parameterized with experimental imaging data, to describe doxorubicin uptake and predict subsequent population dynamics. We found that a three compartment model can describe doxorubicin pharmacokinetics, and pharmacokinetic parameters vary significantly among the cell lines investigated. The proposed model effectively captures population dynamics and translates well to a predictive framework. In a representative cell line (SUM-149PT) treated for 12 hours with doxorubicin, the mean percent errors of the best-fit and predicted models were 14% (±10%) and 16% (±12%), which are notable considering these statistics represent errors over 30 days following treatment. More generally, this work provides both a template for studies quantitatively investigating treatment response and a scalable approach toward predictions of tumor response in vivo.

PMID:
28720897
PMCID:
PMC5516013
DOI:
10.1038/s41598-017-05902-z
[Indexed for MEDLINE]
Free PMC Article

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