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

Hermann B Frieboes, Bryan R Smith, Zhihui Wang, Masakatsu Kotsuma, Ken Ito, Armin Day, Benjamin Cahill, Colin Flinders, Shannon M Mumenthaler, Parag Mallick, Eman Simbawa, A S Al-Fhaid, S R Mahmoud, Sanjiv S Gambhir, Vittorio Cristini

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)e0129433
JournalPLoS ONE
Issue number6
StatePublished - 2015
Externally publishedYes


  • Animals
  • Antibiotics, Antineoplastic
  • Cell Survival
  • Doxorubicin
  • Drug Resistance, Neoplasm
  • Fibroblasts
  • Lymphoma, Non-Hodgkin
  • Mice
  • Models, Theoretical
  • Tumor Cells, Cultured
  • Xenograft Model Antitumor Assays
  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.


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