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Data and implication based comparison of two chronic myeloid leukemia models.

R A Everett1, Y Zhao, K B Flores

  • 1School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, United States. rarodger@asu.edu.

Mathematical Biosciences and Engineering : MBE
|November 20, 2013
PubMed
Summary

A multi-scale model accurately predicts drug resistance in chronic myeloid leukemia (CML) by incorporating sub-cellular dynamics, unlike simpler models. This approach enhances parameter estimation and clinical data modeling for CML treatment.

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Area of Science:

  • Mathematical modeling
  • Oncology
  • Hematology

Background:

  • Chronic myeloid leukemia (CML) is a hematopoietic stem cell disorder.
  • Targeted molecular therapy with imatinib is the current standard treatment for CML.
  • Mathematical models are used to understand CML dynamics and treatment responses.

Purpose of the Study:

  • To compare a multi-scale model (Model 1) with a simple cell competition model (Model 2) for describing CML treatment.
  • To analyze parameter estimation differences and predictive capacity for drug resistance between the two models.
  • To evaluate the biological relevance and accuracy of each model using clinical data.

Main Methods:

  • Development and comparison of two mathematical models: a multi-scale model (Model 1) and a simple cell competition model (Model 2).

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  • Analysis of model parameter estimation using clinical data from CML patients.
  • Assessment of each model's ability to predict drug resistance and accurately represent clinical data.

    • Main Results:

    • Both models fit the clinical data, but Model 1 demonstrated greater biological relevance.
    • Model 1 yielded parameter estimates consistent with literature values, while Model 2's were unrealistic.
    • Model 1 successfully predicted long-term drug resistance, including increased leukemic cells and BCR-ABL/ABL levels, which Model 2 could not.

    Conclusions:

    • Incorporating sub-cellular mechanisms, such as BCR-ABL dynamics, into mathematical models of CML enhances biological relevance.
    • Multi-scale modeling improves the accuracy of parameter estimation in CML treatment studies.
    • Mathematical models that include sub-cellular details can potentially predict long-term drug resistance in CML patients.