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

Hermann B Frieboes1, Bryan R Smith2, Zhihui Wang3

  • 1Department 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.

Plos One
|June 11, 2015
PubMed

Insights

Mathematical modeling combined with mouse experiments quantifies chemotherapy drug resistance. The study establishes a predictive model for lymphoma drug therapy, identifying tissue blood volume fraction as a key resistance factor.

Area of Science:

  • Oncology
  • Mathematical Biology
  • Pharmacology

Background:

  • Chemotherapy drug resistance is a complex challenge influenced by both cellular and tissue-level factors.
  • Understanding the interplay between these factors is crucial for improving treatment efficacy.
  • Current experimental methods alone struggle to fully elucidate these contributions.

Purpose of the Study:

  • To quantify the relative roles of intrinsic cellular and tissue-scale physiological factors in chemotherapy drug resistance.
  • To develop and validate a mechanistic mathematical model integrating experimental data for predictive therapy assessment.
  • To identify key physiological parameters contributing to drug resistance in lymphoma.

Main Methods:

  • Experiments were conducted in cell cultures and in mice using drug-sensitive and drug-resistant lymphoma cell lines (Eµ-myc/Arf-/- and Eµ-myc/p53-/-).
  • A mechanistic mathematical model was calibrated and validated using experimental data, including tumor drug transport characteristics (blood volume fraction, vessel radius, diffusion distance) and cell culture drug response.
  • The model predicted drug response in mice based on these integrated inputs.

Main Results:

  • The mathematical model reliably predicted the fraction of dead tumor volume in mice.
  • The model demonstrated proof-of-principle for predictive quantification of lymphoma drug therapy.
  • In vitro cytotoxic response data, when inputted into the model, enabled prediction of in vivo treatment efficacy.
  • Tissue blood volume fraction emerged as the most sensitive parameter and a primary contributor to drug resistance.

Conclusions:

  • A combined mathematical modeling and experimental approach can effectively quantify contributions to chemotherapy drug resistance.
  • The developed model provides a predictive framework for lymphoma drug therapy, integrating cellular and tissue-scale parameters.
  • Tissue blood volume fraction is a critical determinant of drug resistance, highlighting its importance in therapeutic strategies.

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