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PLoS One. 2015 Jun 10;10(6):e0129433. doi: 10.1371/journal.pone.0129433. eCollection 2015.

Predictive Modeling of Drug Response in Non-Hodgkin's Lymphoma.

Author information

1
Department of Bioengineering, University of Louisville, Louisville, KY, 40202, United States of America; James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, United States of America; Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, United States of America.
2
Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA, 94305, United States of America.
3
Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, United States of America.
4
Department of Bioengineering, University of Louisville, Louisville, KY, 40202, United States of America.
5
Department of Biological Chemistry, University of California at Los Angeles, Los Angeles, CA, 90095, United States of America; Center for Applied Molecular Medicine, University of Southern California, Los Angeles, CA, 90033, United States of America.
6
Center for Applied Molecular Medicine, University of Southern California, Los Angeles, CA, 90033, United States of America.
7
Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
8
Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA, 94305, United States of America; Department of Bioengineering, Stanford University, Stanford, CA, 94305, United States of America; Department of Materials Science & Engineering, and Bio-X, Stanford University, Stanford, CA, 94305, United States of America.
9
Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, United States of America; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM, 87131, United States of America.

Abstract

We combine mathematical modeling with experiments in living mice to quantify the relative roles of intrinsic cellular vs. tissue-scale physiological contributors to chemotherapy drug resistance, which are difficult to understand solely through experimentation. Experiments in cell culture and in mice with drug-sensitive (Eµ-myc/Arf-/-) and drug-resistant (Eµ-myc/p53-/-) lymphoma cell lines were conducted to calibrate and validate a mechanistic mathematical model. Inputs to inform the model include tumor drug transport characteristics, such as blood volume fraction, average geometric mean blood vessel radius, drug diffusion penetration distance, and drug response in cell culture. Model results show that the drug response in mice, represented by the fraction of dead tumor volume, can be reliably predicted from these inputs. Hence, a proof-of-principle for predictive quantification of lymphoma drug therapy was established based on both cellular and tissue-scale physiological contributions. We further demonstrate that, if the in vitro cytotoxic response of a specific cancer cell line under chemotherapy is known, the model is then able to predict the treatment efficacy in vivo. Lastly, tissue blood volume fraction was determined to be the most sensitive model parameter and a primary contributor to drug resistance.

PMID:
26061425
PMCID:
PMC4464754
DOI:
10.1371/journal.pone.0129433
[Indexed for MEDLINE]
Free PMC Article

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